1. Import packages and load data¶
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
import xgboost as xgb
from xgboost import XGBClassifier
# import logistic regression libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_curve, roc_auc_score, RocCurveDisplay, auc
# import classification tree libraries
from sklearn.tree import DecisionTreeClassifier, plot_tree
# import random forest libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier
# import cross validation libraries
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.preprocessing import StandardScaler
# load data
df = pd.read_csv(r"C:\Users\woowe\Downloads\hospital_readmissions.csv")
print(df.head())
age time_in_hospital n_lab_procedures n_procedures n_medications \
0 [70-80) 8 72 1 18
1 [70-80) 3 34 2 13
2 [50-60) 5 45 0 18
3 [70-80) 2 36 0 12
4 [60-70) 1 42 0 7
n_outpatient n_inpatient n_emergency medical_specialty diag_1 \
0 2 0 0 Missing Circulatory
1 0 0 0 Other Other
2 0 0 0 Missing Circulatory
3 1 0 0 Missing Circulatory
4 0 0 0 InternalMedicine Other
diag_2 diag_3 glucose_test A1Ctest change diabetes_med \
0 Respiratory Other no no no yes
1 Other Other no no no yes
2 Circulatory Circulatory no no yes yes
3 Other Diabetes no no yes yes
4 Circulatory Respiratory no no no yes
readmitted
0 no
1 no
2 yes
3 yes
4 no
Exploratory Data Analysis¶
Data quantity¶
print("The raw dataset has {} rows and {} columns".format(df.shape[0], df.shape[1]))
The raw dataset has 25000 rows and 17 columns
Null values¶
# check null values
missing_counts = df.isnull().sum()
print("Count of Missing Values")
print(missing_counts)
Count of Missing Values age 0 time_in_hospital 0 n_lab_procedures 0 n_procedures 0 n_medications 0 n_outpatient 0 n_inpatient 0 n_emergency 0 medical_specialty 0 diag_1 0 diag_2 0 diag_3 0 glucose_test 0 A1Ctest 0 change 0 diabetes_med 0 readmitted 0 dtype: int64
Data types¶
# check data types
df.dtypes
age object time_in_hospital int64 n_lab_procedures int64 n_procedures int64 n_medications int64 n_outpatient int64 n_inpatient int64 n_emergency int64 medical_specialty object diag_1 object diag_2 object diag_3 object glucose_test object A1Ctest object change object diabetes_med object readmitted object dtype: object
Univariate Distribution¶
# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns = df.select_dtypes(include=['int64']).columns.tolist()
cat_columns = df.select_dtypes(include=['object']).columns.tolist()
# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns, 1):
plt.subplot(4, 5, i)
sns.histplot(df[column], kde=False)
plt.title(column)
plt.tight_layout()
plt.show()
# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns, 1):
plt.subplot(1, len(cat_columns), i)
sns.countplot(x=column, data=df)
plt.title(column)
plt.tight_layout()
plt.show()
Summary table¶
# summary table of dataset
df.describe()
| time_in_hospital | n_lab_procedures | n_procedures | n_medications | n_outpatient | n_inpatient | n_emergency | |
|---|---|---|---|---|---|---|---|
| count | 25000.00000 | 25000.00000 | 25000.000000 | 25000.000000 | 25000.000000 | 25000.000000 | 25000.000000 |
| mean | 4.45332 | 43.24076 | 1.352360 | 16.252400 | 0.366400 | 0.615960 | 0.186600 |
| std | 3.00147 | 19.81862 | 1.715179 | 8.060532 | 1.195478 | 1.177951 | 0.885873 |
| min | 1.00000 | 1.00000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 2.00000 | 31.00000 | 0.000000 | 11.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 4.00000 | 44.00000 | 1.000000 | 15.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 6.00000 | 57.00000 | 2.000000 | 20.000000 | 0.000000 | 1.000000 | 0.000000 |
| max | 14.00000 | 113.00000 | 6.000000 | 79.000000 | 33.000000 | 15.000000 | 64.000000 |
Check outliers¶
# check outliers
for column in df.select_dtypes(include=['number']).columns:
plt.figure(figsize=(10, 6)) # Set figure size for each plot
sns.boxplot(x=df[column]) # Create boxplot
plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20}) # Set title
plt.xlabel(column) # Label x-axis
plt.show() # Display the plot
We noted that there were many outliers for n_lab_procedures, n_medications, n_outpatient, n_inpatient and n_emergency. However, since they are not data entry mistakes and are valid values, we decided to keep them.
2. Feature Engineering¶
# Create new interaction term
df['severity'] = df['time_in_hospital'] * (df['n_lab_procedures'] + 32 * df['n_procedures'])
# Log transform skewed variables
df['log_time_in_hospital'] = np.log1p(df['time_in_hospital'])
df['log_n_procedures'] = np.log1p(df['n_procedures'])
df['log_n_inpatient'] = np.log1p(df['n_inpatient'])
df['log_severity'] = np.log1p(df['severity'])
# Drop the original columns to prevent multicollinearity issues
df.drop(["time_in_hospital", "n_procedures", "n_inpatient", "severity"], axis = 1, inplace = True)
df.isnull().sum()
age 0 n_lab_procedures 0 n_medications 0 n_outpatient 0 n_emergency 0 medical_specialty 0 diag_1 0 diag_2 0 diag_3 0 glucose_test 0 A1Ctest 0 change 0 diabetes_med 0 readmitted 0 log_time_in_hospital 0 log_n_procedures 0 log_n_inpatient 0 log_severity 0 dtype: int64
3. Data Pre-processing¶
# one-hot encode independent variables
df_encoded = pd.get_dummies(df, columns=[
'age', 'medical_specialty', 'diag_1', 'diag_2', 'diag_3', 'glucose_test', 'A1Ctest', 'change', 'diabetes_med'
], drop_first=True)
# remove '[' symbol in age values [70-80) since it caused problems for decision trees
df_encoded.columns = df_encoded.columns.str.replace('[', '')
# one-hot readmission variable
df_new = pd.get_dummies(df_encoded, columns = ['readmitted'], drop_first = True, dtype = int)
df_new.head()
| n_lab_procedures | n_medications | n_outpatient | n_emergency | log_time_in_hospital | log_n_procedures | log_n_inpatient | log_severity | age_50-60) | age_60-70) | ... | diag_3_Musculoskeletal | diag_3_Other | diag_3_Respiratory | glucose_test_no | glucose_test_normal | A1Ctest_no | A1Ctest_normal | change_yes | diabetes_med_yes | readmitted_yes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 72 | 18 | 2 | 0 | 2.197225 | 0.693147 | 0.0 | 6.725034 | False | False | ... | False | True | False | True | False | True | False | False | True | 0 |
| 1 | 34 | 13 | 0 | 0 | 1.386294 | 1.098612 | 0.0 | 5.686975 | False | False | ... | False | True | False | True | False | True | False | False | True | 0 |
| 2 | 45 | 18 | 0 | 0 | 1.791759 | 0.000000 | 0.0 | 5.420535 | True | False | ... | False | False | False | True | False | True | False | True | True | 1 |
| 3 | 36 | 12 | 1 | 0 | 1.098612 | 0.000000 | 0.0 | 4.290459 | False | False | ... | False | False | False | True | False | True | False | True | True | 1 |
| 4 | 42 | 7 | 0 | 0 | 0.693147 | 0.000000 | 0.0 | 3.761200 | False | True | ... | False | False | True | True | False | True | False | False | True | 0 |
5 rows × 47 columns
# Splitting the dataset into features (X) and target (y)
X = df_new.drop('readmitted_yes', axis=1)
y = df_new['readmitted_yes']
# Splitting into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
4. Model Training¶
cv = StratifiedKFold(n_splits=10, shuffle = True, random_state = 42)
# Report GridSearchCV results
def report_GridSearchCV_results(grid):
print("- Best combination of hyperparameters:\n", grid.best_params_, "\n")
print("- Best mean_test_score:\n", grid.best_score_, "\n")
scores = []
for i in range(grid.n_splits_):
scores.append(grid.cv_results_['split{}_test_score'.format(i)][grid.best_index_])
print("- Score by fold for best estimator:\n", scores, "\n")
# View top 10 hyperparameter combinations by mean_test_score (mean AUC on validation set)
print("- Top 10 hyperparameter combinations by mean_test_score:")
display(pd.DataFrame(grid.cv_results_)[["rank_test_score", "mean_test_score"]
+ ["param_" + param for param in grid.param_grid]]\
.sort_values(by = "mean_test_score", ascending = False)\
.set_index("rank_test_score").head(10))
return None
# Compare training dataset performance vs validation dataset performance
def compare_performance(grid):
# retrieve training and validation scores
train_scores=grid.cv_results_['mean_train_score']
val_scores=grid.cv_results_['mean_test_score']
# limit to 10 rows
train_scores_limited=train_scores[:10]
val_scores_limited=val_scores[:10]
# create dataframe to store scores
all_scores=pd.DataFrame({
"train_AUC": train_scores_limited,
"val_AUC": val_scores_limited
}, index=range(1,11))
mean_scores=pd.DataFrame({
"train_AUC": [train_scores_limited.mean()],
"val_AUC": [val_scores_limited.mean()]
}, index=["Mean"])
all_scores_combined=pd.concat([all_scores, mean_scores])
return all_scores_combined
# evaluate model on test set
def evaluate_model(best_model, X_test_scaled, y_test):
"""
Parameters:
- best_model: The best estimator from grid search
- X_test_scaled: Scaled test data
- y_test: True labels for test set
"""
# predict probabilities and labels
y_prob=best_model.predict_proba(X_test_scaled)[:,1]
y_pred=best_model.predict(X_test_scaled)
# metrics
test_auc=roc_auc_score(y_test, y_prob)
accuracy=accuracy_score(y_test, y_pred)
conf_matrix=confusion_matrix(y_test, y_pred)
classification_rep=classification_report(y_test, y_pred)
# print metrics
print(f"Test AUC: {test_auc:.2f}")
print(f'Accuracy: {accuracy:.2f}')
print('Confusion Matrix:'); print(conf_matrix)
disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
disp.plot()
plt.show()
print('Classification Report:')
print(classification_rep)
# Plot ROC curve on test set
def plot_roc_curve(best_model, X_test_scaled, y_test):
"""
Parameters:
- best_model: The best estimator from grid search
- X_test_scaled: Scaled test features
- y_test: True labels for the test set
"""
# predict probabilities
y_prob=best_model.predict_proba(X_test_scaled)[:,1]
# compute roc curve
fpr, tpr, thresholds= roc_curve(y_test, y_prob)
roc_auc=auc(fpr, tpr)
# plot roc curve
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc='lower right')
plt.show()
# Plot average feature importance across CV folds
def plot_feature_importance_chart(model, X_train, y_train, cv, model_name):
train_predictions_std = []
feature_importances = []
# Iterate over each fold
for train_idx, val_idx in cv.split(X_train, y_train):
X_train_cv, X_val_cv = X_train.iloc[train_idx], X_train.iloc[val_idx]
y_train_cv, y_val_cv = y_train.iloc[train_idx], y_train.iloc[val_idx]
# Fit model on current fold
model.fit(X_train_cv, y_train_cv)
# Store feature importances for this fold
feature_importances.append(model.feature_importances_)
val_pred_proba = model.predict_proba(X_val_cv)[:, 1]
train_predictions_std.append(np.std(val_pred_proba))
avg_feature_importance = np.mean(feature_importances, axis=0)
# Sort features by importance (descending order)
feature_names = X_train.columns
sorted_indices = np.argsort(avg_feature_importance)[::-1] # Descending order
sorted_importances = avg_feature_importance[sorted_indices]
sorted_feature_names = feature_names[sorted_indices]
# Plot the sorted feature importances
plt.figure(figsize=(10, 8))
plt.barh(sorted_feature_names, sorted_importances, color='skyblue')
plt.xlabel("Average Coefficient (Feature Importance)")
plt.title("Average Feature Importance Across Cross-Validation Folds")
plt.gca().invert_yaxis() # Highest importance at the top
plt.grid(axis='x', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()
Classification Tree¶
Pre-pruning¶
# Initialize model
classificationtree = DecisionTreeClassifier(random_state=42)
# Define the hyperparameter grid
clf_param_grid = {
'max_depth': [2, 3, 4],
'min_samples_leaf': [500, 1000, 2000],
'max_leaf_nodes': [None, 5, 10, 15]
}
# Create a GridSearchCV object
grid_search_clf = GridSearchCV(estimator=classificationtree, param_grid=clf_param_grid, cv=cv, scoring='roc_auc', verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
grid_search_clf.fit(X_train, y_train)
Fitting 10 folds for each of 36 candidates, totalling 360 fits [CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.614) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.616, test=0.595) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.613, test=0.622) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.613) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.616) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.627) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.604) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.614, test=0.610) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.619) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.615, test=0.617) total time= 0.0s [CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.616, test=0.611) total time= 0.0s [CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.618, test=0.599) total time= 0.0s [CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.616, test=0.615) total time= 0.0s [CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.622) total time= 0.0s [CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.625) total time= 0.0s [CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time= 0.0s [CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.615, test=0.624) total time= 0.0s [CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.617, test=0.609) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.616) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.614) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.625, test=0.635) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.627, test=0.629) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.627, test=0.639) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.629, test=0.605) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.622, test=0.613) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.629, test=0.624) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.627, test=0.618) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.612) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.625, test=0.637) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.626, test=0.630) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.627, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.627, test=0.638) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.615) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.626, test=0.631) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.628, test=0.626) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.615) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.608) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.623, test=0.634) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.626) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.634) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.610) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.619, test=0.611) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.626, test=0.622) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.615) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.608) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.634) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.626) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.634) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.610) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.628) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.626, test=0.622) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.616) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.614) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.625, test=0.635) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.627, test=0.629) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.627, test=0.639) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.629, test=0.605) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.622, test=0.613) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.629, test=0.624) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.627, test=0.618) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.612) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.625, test=0.637) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.626, test=0.630) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.627, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.627, test=0.638) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.615) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.626, test=0.631) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.628, test=0.626) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.616) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.614) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.625, test=0.635) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.627, test=0.629) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.627, test=0.639) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.629, test=0.605) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.622, test=0.613) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.628, test=0.631) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.629, test=0.624) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.627, test=0.618) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.612) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.625, test=0.637) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.626, test=0.630) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.627, test=0.631) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.627, test=0.638) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.615) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.626, test=0.631) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.628, test=0.626) total time= 0.0s [CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time= 0.0s [CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time= 0.0s [CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time= 0.0s [CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time= 0.0s [CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time= 0.0s [CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time= 0.0s [CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time= 0.0s [CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time= 0.0s [CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time= 0.0s [CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.635, test=0.624) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.636, test=0.614) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.632, test=0.643) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.634, test=0.635) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.635, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.633, test=0.640) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.637, test=0.608) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.632, test=0.633) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.635, test=0.634) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=500;, score=(train=0.636, test=0.624) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.634, test=0.621) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.635, test=0.612) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.632, test=0.643) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.633, test=0.635) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.632, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.631, test=0.639) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.635, test=0.615) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.632, test=0.633) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.633, test=0.631) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1000;, score=(train=0.634, test=0.627) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.624, test=0.621) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.626, test=0.604) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.624, test=0.623) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.624, test=0.627) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.626, test=0.608) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.615) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.608) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.623, test=0.634) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.626) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.631) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.624, test=0.634) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.610) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.619, test=0.611) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.625, test=0.629) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=500;, score=(train=0.626, test=0.622) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.615) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.608) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.634) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.626) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.631) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.624, test=0.634) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.610) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.623, test=0.628) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.625, test=0.629) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1000;, score=(train=0.626, test=0.622) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.619) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.603) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.621) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.626) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.633) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.635) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.623, test=0.605) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.620, test=0.631) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.621, test=0.629) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2000;, score=(train=0.622, test=0.612) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.635, test=0.624) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.636, test=0.614) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.632, test=0.643) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.634, test=0.636) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.633, test=0.638) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.632, test=0.639) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.636, test=0.610) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.632, test=0.633) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.634, test=0.635) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=500;, score=(train=0.635, test=0.625) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.634, test=0.621) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.635, test=0.612) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.632, test=0.643) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.633, test=0.635) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.632, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.631, test=0.639) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.635, test=0.615) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.632, test=0.633) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.633, test=0.631) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1000;, score=(train=0.634, test=0.627) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.624, test=0.621) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.626, test=0.604) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.624, test=0.623) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.624, test=0.627) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.626, test=0.608) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.635, test=0.624) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.636, test=0.614) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.632, test=0.643) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.634, test=0.635) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.635, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.633, test=0.640) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.637, test=0.608) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.632, test=0.633) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.635, test=0.634) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=500;, score=(train=0.636, test=0.624) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.634, test=0.621) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.635, test=0.612) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.632, test=0.643) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.633, test=0.635) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.632, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.631, test=0.639) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.635, test=0.615) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.632, test=0.633) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.633, test=0.631) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1000;, score=(train=0.634, test=0.627) total time= 0.0s [CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.624, test=0.621) total time= 0.0s [CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.626, test=0.604) total time= 0.0s [CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.624, test=0.623) total time= 0.0s [CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.624, test=0.627) total time= 0.0s [CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.637) total time= 0.0s [CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time= 0.0s [CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.626, test=0.608) total time= 0.0s [CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time= 0.0s [CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.623, test=0.632) total time= 0.0s [CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time= 0.0s
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=DecisionTreeClassifier(random_state=42),
param_grid={'max_depth': [2, 3, 4],
'max_leaf_nodes': [None, 5, 10, 15],
'min_samples_leaf': [500, 1000, 2000]},
return_train_score=True, scoring='roc_auc', verbose=4)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=DecisionTreeClassifier(random_state=42),
param_grid={'max_depth': [2, 3, 4],
'max_leaf_nodes': [None, 5, 10, 15],
'min_samples_leaf': [500, 1000, 2000]},
return_train_score=True, scoring='roc_auc', verbose=4)DecisionTreeClassifier(max_depth=4, min_samples_leaf=1000, random_state=42)
DecisionTreeClassifier(max_depth=4, min_samples_leaf=1000, random_state=42)
report_GridSearchCV_results(grid_search_clf)
- Best combination of hyperparameters:
{'max_depth': 4, 'max_leaf_nodes': None, 'min_samples_leaf': 1000}
- Best mean_test_score:
0.6295579764334001
- Score by fold for best estimator:
[0.6211053235053234, 0.6124514332514333, 0.6434312858312857, 0.6353677313677314, 0.6369251433251433, 0.6393054873054873, 0.6153842122365124, 0.6330801966395186, 0.6313166252633565, 0.6272123256082094]
- Top 10 hyperparameter combinations by mean_test_score:
| mean_test_score | param_max_depth | param_min_samples_leaf | param_max_leaf_nodes | |
|---|---|---|---|---|
| rank_test_score | ||||
| 1 | 0.629558 | 4 | 1000 | 15 |
| 1 | 0.629558 | 4 | 1000 | 10 |
| 1 | 0.629558 | 4 | 1000 | None |
| 4 | 0.629480 | 4 | 500 | 10 |
| 5 | 0.629131 | 4 | 500 | 15 |
| 5 | 0.629131 | 4 | 500 | None |
| 7 | 0.626659 | 3 | 1000 | None |
| 7 | 0.626659 | 3 | 1000 | 15 |
| 7 | 0.626659 | 3 | 1000 | 10 |
| 10 | 0.624173 | 4 | 2000 | 15 |
compare_performance(grid_search_clf)
| train_AUC | val_AUC | |
|---|---|---|
| 1 | 0.614551 | 0.613645 |
| 2 | 0.614551 | 0.613645 |
| 3 | 0.615819 | 0.615886 |
| 4 | 0.614551 | 0.613645 |
| 5 | 0.614551 | 0.613645 |
| 6 | 0.615819 | 0.615886 |
| 7 | 0.614551 | 0.613645 |
| 8 | 0.614551 | 0.613645 |
| 9 | 0.615819 | 0.615886 |
| 10 | 0.614551 | 0.613645 |
| Mean | 0.614932 | 0.614317 |
best_model_clf=grid_search_clf.best_estimator_
plot_feature_importance_chart(best_model_clf, X_train, y_train, cv, "Pre-pruned Classification Tree")
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_clf, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
Create interaction term 'severity'¶
- The new column severity reflects the interaction between time spent in the hospital and the combined number of lab and medical procedures.
- A higher value of severity could indicate a more serious condition, as it implies that patients with longer hospital stays and more procedures are likely to have greater health complexities.
- The term 32 * df['n_procedures'] adds a weighted contribution of the number of procedures, suggesting that each procedure has a significant impact on the overall severity.
# create interaction term 'severity'
df_new = df.copy()
df_new['severity'] = df_new['time_in_hospital'] * (df_new['n_lab_procedures'] + 32 * df_new['n_procedures'])
Transform existing variables¶
For
time_in_hospitalwhich is right-skewed, we apply a regular natural log transform: The transformed variable is namedlog_time_in_hospital.For
n_procedureswhich is right-skewed, we apply a regular natural log transform: The transformed variable is namedlog_n_procedures.For
n_inpatientwhich is right-skewed, we apply a regular natural log transform: The transformed variable is namedlog_n_inpatient.
# Apply a log transform to existing variables
df_new["log_time_in_hospital"] = np.log1p(df_new["time_in_hospital"])
df_new["log_n_procedures"] = np.log1p(df_new["n_procedures"])
df_new["log_n_inpatient"] = np.log1p(df_new["n_inpatient"])
# Drop the original columns to prevent multicollinearity issues
df_new.drop(["time_in_hospital", "n_procedures", "n_inpatient"], axis = 1, inplace = True)
df_new.head()
Exploratory Data Analysis (Processed Data)¶
Data types¶
# check data types
df_new.dtypes
Univariate Distribution¶
# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns_new = df_new.select_dtypes(include=['int64', 'float64']).columns.tolist()
cat_columns_new = df_new.select_dtypes(include=['object']).columns.tolist()
# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns_new, 1):
plt.subplot(4, 5, i)
sns.histplot(df_new[column], kde=False)
plt.title(column)
plt.tight_layout()
plt.show()
# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns_new, 1):
plt.subplot(1, len(cat_columns_new), i)
sns.countplot(x=column, data=df)
plt.title(column)
plt.tight_layout()
plt.show()
Summary table¶
# summary table of dataset
df_new.describe()
Check outliers¶
# check outliers
for column in df_new.select_dtypes(include=['number']).columns:
plt.figure(figsize=(10, 6)) # Set figure size for each plot
sns.boxplot(x=df_new[column]) # Create boxplot
plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20}) # Set title
plt.xlabel(column) # Label x-axis
plt.show() # Display the plot
# one-hotting independent variables
df2 = pd.get_dummies(df_new, columns = ['diabetes_med'], drop_first = True, dtype = int)
df3 = pd.get_dummies(df2, columns = ['age'], drop_first = True, dtype = int)
df4 = pd.get_dummies(df3, columns = ['medical_specialty'], drop_first = True, dtype = int)
df5 = pd.get_dummies(df4, columns = ['diag_1'], drop_first = True, dtype = int)
df6 = pd.get_dummies(df5, columns = ['diag_2'], drop_first = True, dtype = int)
df7 = pd.get_dummies(df6, columns = ['diag_3'], drop_first = True, dtype = int)
df8 = pd.get_dummies(df7, columns = ['glucose_test'], drop_first = True, dtype = int)
df9 = pd.get_dummies(df8, columns = ['A1Ctest'], drop_first = True, dtype = int)
df10 = pd.get_dummies(df9, columns = ['change'], drop_first = True, dtype = int)
# one-hot readmission variable
df11 = pd.get_dummies(df10, columns = ['readmitted'], drop_first = True, dtype = int)
print(df11.head())
# define feature names for x and y datasets
# remove '[' for xgboost model to run successfully
y_name=['readmitted_yes']
x_name=['n_lab_procedures', 'n_medications', 'n_outpatient', 'n_emergency',
'severity', 'log_time_in_hospital', 'log_n_procedures',
'log_n_inpatient', 'diabetes_med_yes', 'age_50-60)', 'age_60-70)',
'age_70-80)', 'age_80-90)', 'age_90-100)',
'medical_specialty_Emergency/Trauma',
'medical_specialty_Family/GeneralPractice',
'medical_specialty_InternalMedicine', 'medical_specialty_Missing',
'medical_specialty_Other', 'medical_specialty_Surgery',
'diag_1_Diabetes', 'diag_1_Digestive', 'diag_1_Injury',
'diag_1_Missing', 'diag_1_Musculoskeletal', 'diag_1_Other',
'diag_1_Respiratory', 'diag_2_Diabetes', 'diag_2_Digestive',
'diag_2_Injury', 'diag_2_Missing', 'diag_2_Musculoskeletal',
'diag_2_Other', 'diag_2_Respiratory', 'diag_3_Diabetes',
'diag_3_Digestive', 'diag_3_Injury', 'diag_3_Missing',
'diag_3_Musculoskeletal', 'diag_3_Other', 'diag_3_Respiratory',
'glucose_test_no', 'glucose_test_normal', 'A1Ctest_no',
'A1Ctest_normal', 'change_yes']
# define features (X) and target (y)
df_X = df11.iloc[:, :-1] # drop 'readmitted_yes'
df_y = df11.iloc[:, -1:] # only store 'readmitted_yes'
# split data points (rows) into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, test_size = 0.2, random_state = 42)
# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Convert scaled features back to DataFrame
df_X_train_scaled = pd.DataFrame(X_train_scaled, columns=x_name).reset_index(drop=True)
df_X_test_scaled = pd.DataFrame(X_test_scaled, columns=x_name).reset_index(drop=True)
df_y_train = pd.DataFrame(y_train, columns=y_name).reset_index(drop=True)
df_y_test = pd.DataFrame(y_test, columns=y_name).reset_index(drop=True)
df_train = pd.concat([df_X_train_scaled, df_y_train], axis = 1)
df_test = pd.concat([df_X_test_scaled, df_y_test], axis = 1)
To check any null values in final datasets
df_train.isnull().sum()
df_test.isnull().sum()
K-fold cross validation is used since there is no class imbalance issue for our target variable.
# K-fold CV splitter
kf10 = KFold(n_splits = 10, shuffle = True, random_state = 42)
# Manually generate CV folds
def get_CV_folds(df, y, X, cv):
train, val = [], []
for train_index, val_index in cv.split(df[X], df[y]):
train.append(df.loc[train_index])
val.append(df.loc[val_index])
return train, val
# Report GridSearchCV results
def report_GridSearchCV_results(grid):
print("- Best combination of hyperparams:\n", grid.best_params_, "\n")
print("- Best mean_test_score:\n", grid.best_score_, "\n")
scores = []
for i in range(grid.n_splits_):
scores.append(grid.cv_results_['split{}_test_score'.format(i)][grid.best_index_])
print("- Score by fold for best estimator:\n", scores, "\n")
# View top 10 hyperparameter combinations by mean_test_score (mean AUC on validation set)
print("- Top 10 hyperparameter combinations by mean_test_score:")
display(pd.DataFrame(grid.cv_results_)[["rank_test_score", "mean_test_score"]
+ ["param_" + param for param in grid.param_grid]]\
.sort_values(by = "mean_test_score", ascending = False)\
.set_index("rank_test_score").head(10))
return None
# Compare training dataset performance vs validation dataset performance
def compare_performance(grid):
# retrieve training and validation scores
train_scores=grid.cv_results_['mean_train_score']
val_scores=grid.cv_results_['mean_test_score']
# limit to 10 rows
train_scores_limited=train_scores[:10]
val_scores_limited=val_scores[:10]
# create dataframe to store scores
all_scores=pd.DataFrame({
"train_AUC": train_scores_limited,
"val_AUC": val_scores_limited
}, index=range(1,11))
mean_scores=pd.DataFrame({
"train_AUC": [train_scores_limited.mean()],
"val_AUC": [val_scores_limited.mean()]
}, index=["Mean"])
all_scores_combined=pd.concat([all_scores, mean_scores])
return all_scores_combined
# Plot histogram of SD of P(Readmitted) when training set varies
def plot_probability_std(estimator, df, y, X, cv, model_name):
train, val = get_CV_folds(df, y, X, cv)
test = val[-1]
prob = pd.DataFrame()
for i in range(cv.n_splits - 1):
train = val[i]
estimator = estimator.fit(train[X], train[y])
prob["Fold {}".format(i+1)] = [pred[1] for pred in estimator.predict_proba(test[X])]
prob_std = prob.apply(lambda x: x.std(ddof=0), axis = 1)
plt.figure(figsize = (8, 5))
plt.hist(prob_std, rwidth = 0.6, bins = np.arange(0, 0.1, 0.01))
plt.title("{} || SD of probability predictions when training set varies".format(model_name), fontsize = 14)
plt.ylabel("Count of test observations", fontsize = 12)
plt.xlabel("SD of P(Readmitted)", fontsize = 12)
plt.show()
return None
# Plot average feature importance across CV folds
def plot_avg_feature_importance(tree, df, y, X, cv, model_name):
train, val = get_CV_folds(df, y, X, cv)
impt = pd.DataFrame()
for i in range(cv.n_splits):
df_train = train[i]
tree = tree.fit(df_train[X], df_train[y])
impt[str(i)] = tree.feature_importances_
ft = list(zip(X, impt.mean(axis = 1)))
ft.sort(key = lambda x: x[1])
plt.figure(figsize = (8, 10))
features, importances = [x[0] for x in ft], [x[1] for x in ft]
plt.barh(features, importances)
plt.title("{} || Avg. feature importance across CV folds".format(model_name), fontsize = 14)
plt.show()
return None
# evaluate model on test set
def evaluate_model(best_model, X_test_scaled, y_test):
"""
Parameters:
- best_model: The best estimator from grid search
- X_test_scaled: Scaled test data
- y_test: True labels for test set
"""
# predict probabilities and labels
y_prob=best_model.predict_proba(X_test_scaled)[:,1]
y_pred=best_model.predict(X_test_scaled)
# metrics
test_auc=roc_auc_score(y_test, y_prob)
accuracy=accuracy_score(y_test, y_pred)
conf_matrix=confusion_matrix(y_test, y_pred)
classification_rep=classification_report(y_test, y_pred)
# print metrics
print(f"Test AUC: {test_auc:.2f}")
print(f'Accuracy: {accuracy:.2f}')
print('Confusion Matrix:'); print(conf_matrix)
disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
disp.plot()
plt.show()
print('Classification Report:')
print(classification_rep)
# Plot ROC curve on test set
def plot_roc_curve(best_model, X_test_scaled, y_test):
"""
Parameters:
- best_model: The best estimator from grid search
- X_test_scaled: Scaled test features
- y_test: True labels for the test set
"""
# predict probabilities
y_prob=best_model.predict_proba(X_test_scaled)[:,1]
# compute roc curve
fpr, tpr, thresholds= roc_curve(y_test, y_prob)
roc_auc=auc(fpr, tpr)
# plot roc curve
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc='lower right')
plt.show()
Classification Tree¶
Pre-Pruning¶
# Initialize model
classificationtree = DecisionTreeClassifier(random_state=42)
# Define the hyperparameter grid
clf_param_grid = {
'max_depth': [2, 3, 4],
'min_samples_leaf': [500, 1000, 2000],
'max_leaf_nodes': [None, 5, 10, 15]
}
# Create a GridSearchCV object
grid_search_clf = GridSearchCV(estimator=classificationtree, param_grid=clf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
grid_search_clf.fit(df_train[x_name], df_train[y_name])
Model Performance¶
report_GridSearchCV_results(grid_search_clf)
compare_performance(grid_search_clf)
best_model_clf=grid_search_clf.best_estimator_
plot_probability_std(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")
plot_avg_feature_importance(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")
evaluate_model(best_model_clf, df_X_test_scaled, df_y_test)
plot_roc_curve(best_model_clf, df_X_test_scaled, df_y_test)
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_clf, filled=True, feature_names=x_name, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
Post-pruning¶
# Get effective alphas for pruning
path = classificationtree.cost_complexity_pruning_path(X_train_scaled, y_train)
ccp_alphas=path.ccp_alphas
impurities=path.impurities
# Define the hyperparameter grid
post_prune_param_grid = {
'ccp_alpha': ccp_alphas
}
# Create a GridSearchCV object
grid_search_post_prune = GridSearchCV(estimator=classificationtree, param_grid=post_prune_param_grid, scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
grid_search_post_prune.fit(df_train[x_name], df_train[y_name])
Model Performance¶
report_GridSearchCV_results(grid_search_post_prune)
compare_performance(grid_search_post_prune)
best_model_post_prune=grid_search_post_prune.best_estimator_
plot_probability_std(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")
plot_avg_feature_importance(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")
evaluate_model(best_model_post_prune, df_X_test_scaled, df_y_test)
plot_roc_curve(best_model_post_prune, df_X_test_scaled, df_y_test)
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_post_prune, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
Random Forest Model (with regularization)¶
# Initialize model
randomforest = RandomForestClassifier(max_depth = 6, random_state = 42, bootstrap=True)
# Define the hyperparameter grid
rf_param_grid = {
'max_depth': [2, 3, 4],
'min_samples_leaf': [500, 1000, 2000],
'max_features': [2, 3],
}
# Create a GridSearchCV object
grid_search_rf = GridSearchCV(estimator=randomforest, param_grid=rf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
# To resolve error: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of
# y to (n_samples,), for example using ravel().return fit_method(estimator, *args, **kwargs)
grid_search_rf.fit(df_train[x_name], df_train[y_name].values.ravel())
Model Performance¶
report_GridSearchCV_results(grid_search_rf)
compare_performance(grid_search_rf)
best_model_rf=grid_search_rf.best_estimator_
plot_probability_std(best_model_rf, df_train, y_name, x_name, kf10, "Bagged Random Forest")
plot_avg_feature_importance(best_model_rf, df_train, y_name, x_name, kf10, "")
evaluate_model(best_model_rf, df_X_test_scaled, df_y_test)
plot_roc_curve(best_model_rf, df_X_test_scaled, df_y_test)
XGBoost (With regularization)¶
# Initialize model
xgb_model = xgb.XGBClassifier(random_state = 42)
# Define the hyperparameter grid
xgb_param_grid = {
'colsample_bytree': [0.3, 0.7],
'n_estimators': [50, 100, 200],
'max_depth': [2, 5, 10],
'alpha': [0, 0.1, 1], # Alpha/lasso regularisation
'lambda': [0, 0.1, 1], # Lambda/ridge regularisation
'learning_rate': [0.01, 0.05]
}
# Create a GridSearchCV object
grid_search_xgb = GridSearchCV(param_grid=xgb_param_grid, estimator=xgb_model,
scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
grid_search_xgb.fit(df_train[x_name], df_train[y_name])
Model Performance¶
report_GridSearchCV_results(grid_search_xgb)
compare_performance(grid_search_xgb)
best_model_xgb=grid_search_xgb.best_estimator_
plot_probability_std(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")
plot_avg_feature_importance(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")
evaluate_model(best_model_xgb, df_X_test_scaled, df_y_test)
plot_roc_curve(best_model_xgb, df_X_test_scaled, df_y_test)
evaluate_model(best_model_clf, X_test, y_test)
Test AUC: 0.63 Accuracy: 0.61 Confusion Matrix: [[2669 1331] [1622 1878]]
Classification Report:
precision recall f1-score support
0 0.62 0.67 0.64 4000
1 0.59 0.54 0.56 3500
accuracy 0.61 7500
macro avg 0.60 0.60 0.60 7500
weighted avg 0.60 0.61 0.60 7500
plot_roc_curve(best_model_clf, X_test, y_test)
Post-pruning¶
# Get effective alphas for pruning
path = classificationtree.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas=path.ccp_alphas
impurities=path.impurities
# Define the hyperparameter grid
post_prune_param_grid = {
'ccp_alpha': ccp_alphas
}
# Create a GridSearchCV object
grid_search_post_prune = GridSearchCV(estimator=classificationtree, param_grid=post_prune_param_grid, scoring='roc_auc', cv=cv, verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
grid_search_post_prune.fit(X_train, y_train)
Fitting 10 folds for each of 2080 candidates, totalling 20800 fits [CV 1/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=2.742857142857142e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.1746031746031745e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.522) total time= 0.6s [CV 8/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.26530612244898e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.3333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.4632034632034636e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.5164835164835154e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.537414965986395e-05;, score=(train=1.000, test=0.537) total time= 0.5s [CV 1/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.585434173669469e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.597883597883599e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.609022556390979e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.643892339544515e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.663003663003665e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.553) total time= 0.6s [CV 6/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.6734693877551016e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.686635944700458e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.69047619047619e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.6940836940836915e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.697478991596638e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.70927318295739e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.751803751803745e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.761904761904758e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.3s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=3.809523809523809e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.219780219780218e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time= 0.5s [CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.550) total time= 0.6s [CV 6/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.2857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.5s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.57142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.6300366300366335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.550) total time= 0.4s [CV 6/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.675324675324675e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.5s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.6s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.5s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.6s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.3s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.761904761904762e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.832080200501256e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.835164835164835e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.543) total time= 0.5s [CV 7/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.857142857142856e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.89795918367347e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.5s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.8979591836734704e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.538) total time= 0.3s [CV 3/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.9266022198353e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=4.96894409937888e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.5s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.5s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.3s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.5s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.543) total time= 0.5s [CV 7/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.0216450216450216e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.543) total time= 0.3s [CV 7/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.538) total time= 0.3s [CV 3/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.538) total time= 0.4s [CV 3/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.0420168067226885e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.079365079365079e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.3s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.3s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.3s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.3s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.3s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.3s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.543) total time= 0.4s [CV 7/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.0793650793650794e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.536) total time= 0.3s [CV 3/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.1020408163265315e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.531) total time= 0.5s [CV 10/10] END ccp_alpha=5.112781954887217e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.117739403453689e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.138978668390431e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.6s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.5s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.5s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.6s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.5s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.142857142857144e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.155462184873953e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.536) total time= 0.3s [CV 3/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.170068027210884e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.194805194805195e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.217391304347825e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2205882352941206e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.224489795918367e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.5s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.5s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.2380952380952384e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.5s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.3s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.3s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.3s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.3s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.274725274725273e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.285714285714285e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.542) total time= 0.4s [CV 7/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.2857142857142876e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.536) total time= 0.3s [CV 3/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.291005291005291e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.3s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.522) total time= 0.3s [CV 4/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.306122448979593e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.307170364936735e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.31512605042017e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.537) total time= 0.5s [CV 3/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.320197044334973e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.322128851540615e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.6s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.3s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.3s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.5s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.333333333333335e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.529) total time= 0.3s [CV 10/10] END ccp_alpha=5.3524253524253536e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.354444339115876e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.3s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.529) total time= 0.5s [CV 10/10] END ccp_alpha=5.357142857142857e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.537) total time= 0.5s [CV 3/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.544) total time= 0.5s [CV 7/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.529) total time= 0.5s [CV 10/10] END ccp_alpha=5.3679653679653665e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.3696145124716554e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time= 0.5s [CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time= 0.5s [CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time= 0.5s [CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time= 0.6s [CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time= 0.5s [CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time= 0.6s [CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time= 0.5s [CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time= 0.5s [CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time= 0.5s [CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time= 0.5s [CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time= 0.5s [CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.529) total time= 0.4s [CV 10/10] END ccp_alpha=5.378151260504203e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.529) total time= 0.6s [CV 10/10] END ccp_alpha=5.387755102040814e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time= 0.3s [CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.396825396825398e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.3s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.3s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.413533834586469e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.544) total time= 0.3s [CV 7/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 10/10] END ccp_alpha=5.428571428571427e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.544) total time= 0.4s [CV 7/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.530) total time= 0.3s [CV 10/10] END ccp_alpha=5.43554006968641e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time= 0.5s [CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time= 0.3s [CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.442176870748301e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.531) total time= 0.5s [CV 10/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.530) total time= 0.5s [CV 1/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.454545454545457e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time= 0.5s [CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time= 0.3s [CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time= 0.3s [CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time= 0.3s [CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time= 0.5s [CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.465838509316772e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.4761904761904794e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.546) total time= 0.3s [CV 7/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.485714285714284e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.49165120593692e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.546) total time= 0.3s [CV 7/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.537) total time= 0.3s [CV 3/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.494505494505498e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.519) total time= 0.3s [CV 8/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.554) total time= 0.3s [CV 6/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.5026455026455e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.5172413793103406e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.536) total time= 0.3s [CV 3/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.5189255189255235e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 2/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.525) total time= 0.3s [CV 4/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.531) total time= 0.4s [CV 10/10] END ccp_alpha=5.529953917050687e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.541125541125537e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.531) total time= 0.3s [CV 10/10] END ccp_alpha=5.5494505494505454e-05;, score=(train=1.000, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.524) total time= 0.3s [CV 2/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=5.555555555555557e-05;, score=(train=1.000, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.555555555555557e-05;, score=(train=0.999, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=5.581395348837214e-05;, score=(train=1.000, test=0.558) total time= 0.3s [CV 9/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.581395348837214e-05;, score=(train=0.999, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=1.000, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.5933484504913106e-05;, score=(train=0.999, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.524) total time= 0.3s [CV 2/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.535) total time= 0.3s [CV 3/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.666666666666669e-05;, score=(train=0.999, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.524) total time= 0.3s [CV 2/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.532) total time= 0.5s [CV 10/10] END ccp_alpha=5.672877846790888e-05;, score=(train=0.999, test=0.528) total time= 0.5s [CV 1/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.559) total time= 0.3s [CV 9/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.687952600394996e-05;, score=(train=0.999, test=0.528) total time= 0.5s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.5s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.5s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.3s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.3s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.3s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.3s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.5s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.3s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.5s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.3s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.5s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.3s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.4s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.3s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.3s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.534) total time= 0.5s [CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.553) total time= 0.3s [CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.532) total time= 0.3s [CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.527) total time= 0.4s [CV 4/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.998, test=0.535) total time= 0.4s [CV 5/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.998, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.561) total time= 0.3s [CV 9/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.998, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=5.788497217068646e-05;, score=(train=0.999, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.999, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 4/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.535) total time= 0.3s [CV 5/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.999, test=0.561) total time= 0.3s [CV 9/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.537) total time= 0.3s [CV 10/10] END ccp_alpha=5.801360544217687e-05;, score=(train=0.998, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.999, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.536) total time= 0.3s [CV 3/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.999, test=0.528) total time= 0.4s [CV 4/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.535) total time= 0.4s [CV 5/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.551) total time= 0.3s [CV 6/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.999, test=0.561) total time= 0.4s [CV 9/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.537) total time= 0.3s [CV 10/10] END ccp_alpha=5.8049886621315224e-05;, score=(train=0.998, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.532) total time= 0.4s [CV 3/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.526) total time= 0.4s [CV 4/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.537) total time= 0.5s [CV 5/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.997, test=0.549) total time= 0.4s [CV 6/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.553) total time= 0.4s [CV 7/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.561) total time= 0.4s [CV 9/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.997, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=5.884275907159196e-05;, score=(train=0.998, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.532) total time= 0.3s [CV 3/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.526) total time= 0.4s [CV 4/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.537) total time= 0.3s [CV 5/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.997, test=0.549) total time= 0.4s [CV 6/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.553) total time= 0.3s [CV 7/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.561) total time= 0.4s [CV 9/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.997, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=5.8863918690005636e-05;, score=(train=0.998, test=0.532) total time= 0.5s [CV 1/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.533) total time= 0.4s [CV 3/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.527) total time= 0.4s [CV 4/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.537) total time= 0.4s [CV 5/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.549) total time= 0.4s [CV 6/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.562) total time= 0.3s [CV 9/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.997, test=0.542) total time= 0.3s [CV 10/10] END ccp_alpha=5.9096459096459164e-05;, score=(train=0.998, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.533) total time= 0.4s [CV 3/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.528) total time= 0.4s [CV 4/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.536) total time= 0.4s [CV 5/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.549) total time= 0.3s [CV 6/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.554) total time= 0.3s [CV 7/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.525) total time= 0.4s [CV 8/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.540) total time= 0.4s [CV 10/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.533) total time= 0.4s [CV 3/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.528) total time= 0.4s [CV 4/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.536) total time= 0.4s [CV 5/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.549) total time= 0.4s [CV 6/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.525) total time= 0.4s [CV 8/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.540) total time= 0.4s [CV 10/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.533) total time= 0.3s [CV 3/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.998, test=0.528) total time= 0.3s [CV 4/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.536) total time= 0.3s [CV 5/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.549) total time= 0.4s [CV 6/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.554) total time= 0.3s [CV 7/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.525) total time= 0.4s [CV 8/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.540) total time= 0.4s [CV 10/10] END ccp_alpha=5.952380952380952e-05;, score=(train=0.997, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time= 0.4s [CV 3/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.528) total time= 0.5s [CV 4/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.535) total time= 0.4s [CV 5/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.548) total time= 0.5s [CV 6/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.541) total time= 0.3s [CV 10/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time= 0.4s [CV 3/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.528) total time= 0.4s [CV 4/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.535) total time= 0.4s [CV 5/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.548) total time= 0.3s [CV 6/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.997, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.541) total time= 0.4s [CV 10/10] END ccp_alpha=5.999999999999994e-05;, score=(train=0.996, test=0.529) total time= 0.5s [CV 1/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.997, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.529) total time= 0.4s [CV 3/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.997, test=0.527) total time= 0.5s [CV 4/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.535) total time= 0.5s [CV 5/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.548) total time= 0.4s [CV 6/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.997, test=0.560) total time= 0.3s [CV 9/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.541) total time= 0.4s [CV 10/10] END ccp_alpha=6.011497211497218e-05;, score=(train=0.996, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.996, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.996, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.544) total time= 0.4s [CV 6/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.553) total time= 0.4s [CV 7/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=6.095238095238095e-05;, score=(train=0.995, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.996, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.996, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.544) total time= 0.6s [CV 6/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.553) total time= 0.4s [CV 7/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=6.095238095238096e-05;, score=(train=0.995, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.544) total time= 0.4s [CV 6/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.553) total time= 0.4s [CV 7/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.544) total time= 0.4s [CV 6/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.553) total time= 0.3s [CV 7/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.996, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.544) total time= 0.4s [CV 6/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.553) total time= 0.4s [CV 7/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.556) total time= 0.3s [CV 9/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=6.0952380952380964e-05;, score=(train=0.995, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 2/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.534) total time= 0.5s [CV 5/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.994, test=0.542) total time= 0.4s [CV 6/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.552) total time= 0.4s [CV 7/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 8/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.553) total time= 0.3s [CV 9/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.535) total time= 0.4s [CV 10/10] END ccp_alpha=6.122448979591836e-05;, score=(train=0.995, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 2/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.525) total time= 0.3s [CV 3/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.994, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.551) total time= 0.4s [CV 7/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.994, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.553) total time= 0.4s [CV 9/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.995, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.158730158730156e-05;, score=(train=0.994, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 2/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=6.185448538389706e-05;, score=(train=0.994, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 2/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 9/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=6.22205228695694e-05;, score=(train=0.994, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 2/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time= 0.6s [CV 9/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.518) total time= 0.3s [CV 2/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 9/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.995, test=0.532) total time= 0.3s [CV 10/10] END ccp_alpha=6.22448979591837e-05;, score=(train=0.994, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 2/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.549) total time= 0.3s [CV 9/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.518) total time= 0.4s [CV 2/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.524) total time= 0.3s [CV 3/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.550) total time= 0.5s [CV 7/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=6.233766233766233e-05;, score=(train=0.994, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.995, test=0.532) total time= 0.4s [CV 10/10] END ccp_alpha=6.318203535594837e-05;, score=(train=0.994, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.550) total time= 0.4s [CV 7/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.995, test=0.532) total time= 0.3s [CV 10/10] END ccp_alpha=6.329670329670328e-05;, score=(train=0.994, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.550) total time= 0.5s [CV 7/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.995, test=0.532) total time= 0.3s [CV 10/10] END ccp_alpha=6.333666333666338e-05;, score=(train=0.994, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.517) total time= 0.3s [CV 2/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time= 0.3s [CV 9/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.533) total time= 0.3s [CV 10/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time= 0.3s [CV 3/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.4e-05;, score=(train=0.995, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.4e-05;, score=(train=0.993, test=0.540) total time= 0.3s [CV 6/10] END ccp_alpha=6.4e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.4e-05;, score=(train=0.993, test=0.512) total time= 0.3s [CV 8/10] END ccp_alpha=6.4e-05;, score=(train=0.995, test=0.549) total time= 0.3s [CV 9/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.4e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.993, test=0.540) total time= 0.3s [CV 6/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.445714285714286e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.540) total time= 0.3s [CV 6/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.549) total time= 0.3s [CV 9/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.534) total time= 0.3s [CV 10/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.517) total time= 0.3s [CV 2/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.540) total time= 0.3s [CV 6/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.995, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.446886446886446e-05;, score=(train=0.994, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.993, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.449848024316108e-05;, score=(train=0.994, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.453781512605049e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.533) total time= 0.3s [CV 10/10] END ccp_alpha=6.470380031711933e-05;, score=(train=0.994, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.533) total time= 0.3s [CV 10/10] END ccp_alpha=6.47352647352647e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.516) total time= 0.3s [CV 2/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.522) total time= 0.4s [CV 3/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.993, test=0.539) total time= 0.3s [CV 6/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.533) total time= 0.3s [CV 10/10] END ccp_alpha=6.491228070175437e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.3s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.3s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.3s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.3s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.3s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.3s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.3s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.6s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.6s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.995, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.530612244897959e-05;, score=(train=0.994, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.520) total time= 0.4s [CV 4/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.995, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.53061224489796e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.993, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.54341736694678e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.993, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.995, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.575963718820863e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.993, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.575963718820864e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.993, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.993, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.59108087679516e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.995, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.603174603174604e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.993, test=0.512) total time= 0.6s [CV 8/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.639455782312926e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.993, test=0.537) total time= 0.6s [CV 6/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.993, test=0.512) total time= 0.6s [CV 8/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.649350649350652e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.537) total time= 0.6s [CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.517) total time= 0.6s [CV 2/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.993, test=0.537) total time= 0.6s [CV 6/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.993, test=0.511) total time= 0.7s [CV 8/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.549) total time= 0.7s [CV 9/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.69384930931376e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time= 0.4s [CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time= 0.6s [CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time= 0.6s [CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.9s [CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time= 1.1s [CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.8s [CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time= 0.7s [CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time= 0.6s [CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time= 0.5s [CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time= 0.5s [CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.536) total time= 0.5s [CV 6/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.722689075630251e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.993, test=0.536) total time= 0.4s [CV 6/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.993, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.740878169449599e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.993, test=0.536) total time= 0.5s [CV 6/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.993, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.763649049363335e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.993, test=0.536) total time= 0.4s [CV 6/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.993, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.766917293233075e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.523) total time= 0.7s [CV 4/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time= 0.6s [CV 7/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.772486772486773e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.993, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.776942355889723e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time= 0.5s [CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time= 0.5s [CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time= 0.5s [CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time= 0.4s [CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.550) total time= 0.5s [CV 9/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.802721088435374e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.550) total time= 0.7s [CV 9/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.994, test=0.533) total time= 0.6s [CV 10/10] END ccp_alpha=6.810677487369215e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time= 0.4s [CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time= 0.4s [CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time= 0.5s [CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time= 0.5s [CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.550) total time= 0.4s [CV 9/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.81704260651629e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.550) total time= 0.5s [CV 9/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.832232095389997e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.550) total time= 0.4s [CV 9/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.83982683982684e-05;, score=(train=0.993, test=0.524) total time= 0.6s [CV 1/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142855e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.6s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.6s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.6s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.6s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.6s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.6s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.7s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.6s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.7s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.6s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.6s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.6s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.6s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.549) total time= 0.6s [CV 9/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.857142857142857e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.85714285714286e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.993, test=0.537) total time= 0.5s [CV 6/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.857142857142864e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.993, test=0.537) total time= 0.4s [CV 6/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.89342403628118e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.993, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.994, test=0.533) total time= 0.4s [CV 10/10] END ccp_alpha=6.914765906362545e-05;, score=(train=0.993, test=0.524) total time= 0.6s [CV 1/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.538) total time= 0.4s [CV 6/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.994, test=0.533) total time= 0.5s [CV 10/10] END ccp_alpha=6.925608953258729e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.993, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.992, test=0.538) total time= 0.4s [CV 6/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.548) total time= 0.8s [CV 9/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.994, test=0.534) total time= 0.7s [CV 10/10] END ccp_alpha=6.926406926406927e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.993, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.993, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.956521739130433e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.993, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.993, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.992, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.965986394557824e-05;, score=(train=0.993, test=0.524) total time= 0.6s [CV 1/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.993, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.993, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.992, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=6.978965027745514e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.993, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.993, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.992, test=0.538) total time= 0.4s [CV 6/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.992, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=6.985994397759106e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.993, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.993, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.992, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.994, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=6.990723562152134e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=7e-05;, score=(train=0.993, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=7e-05;, score=(train=0.993, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7e-05;, score=(train=0.992, test=0.538) total time= 0.4s [CV 6/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7e-05;, score=(train=0.992, test=0.513) total time= 0.6s [CV 8/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7e-05;, score=(train=0.994, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7e-05;, score=(train=0.993, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.993, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.993, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.992, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.994, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.012987012987014e-05;, score=(train=0.993, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.516) total time= 0.4s [CV 2/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.993, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.993, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=7.032967032967031e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.516) total time= 0.5s [CV 2/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.994, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.993, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.992, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.994, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.047791112831765e-05;, score=(train=0.993, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.994, test=0.517) total time= 0.4s [CV 2/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.992, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.992, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.992, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=7.064935064935067e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.992, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.992, test=0.513) total time= 0.7s [CV 8/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.548) total time= 0.6s [CV 9/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=7.07482993197279e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.994, test=0.517) total time= 0.6s [CV 2/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.992, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.992, test=0.513) total time= 0.6s [CV 8/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.549) total time= 0.6s [CV 9/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=7.102040816326529e-05;, score=(train=0.993, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.994, test=0.517) total time= 0.5s [CV 2/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.994, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.992, test=0.540) total time= 0.6s [CV 6/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.549) total time= 0.6s [CV 7/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.10379051842466e-05;, score=(train=0.993, test=0.522) total time= 0.6s [CV 1/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.994, test=0.518) total time= 0.6s [CV 2/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.549) total time= 0.6s [CV 9/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.12087912087912e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.994, test=0.518) total time= 0.4s [CV 2/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.534) total time= 0.6s [CV 10/10] END ccp_alpha=7.122655122655114e-05;, score=(train=0.993, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.994, test=0.518) total time= 0.4s [CV 2/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.992, test=0.521) total time= 0.4s [CV 4/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.534) total time= 0.6s [CV 10/10] END ccp_alpha=7.128652138821629e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time= 0.5s [CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time= 0.5s [CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time= 0.5s [CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time= 0.5s [CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time= 0.7s [CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time= 0.6s [CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.518) total time= 0.5s [CV 2/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.992, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.549) total time= 0.4s [CV 9/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.534) total time= 0.4s [CV 10/10] END ccp_alpha=7.142857142857142e-05;, score=(train=0.993, test=0.523) total time= 0.6s [CV 1/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.994, test=0.518) total time= 0.7s [CV 2/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.994, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.992, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.992, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.549) total time= 0.5s [CV 9/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.142857142857146e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.520) total time= 0.6s [CV 2/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.520) total time= 0.6s [CV 4/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.512) total time= 0.6s [CV 8/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.226001511715796e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.520) total time= 0.6s [CV 2/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.228439763001973e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.520) total time= 0.5s [CV 2/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.538) total time= 0.5s [CV 6/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.512) total time= 0.7s [CV 8/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.993, test=0.534) total time= 0.5s [CV 10/10] END ccp_alpha=7.238095238095236e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.545) total time= 0.6s [CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.991, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.991, test=0.511) total time= 0.5s [CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.991, test=0.511) total time= 0.4s [CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.993, test=0.535) total time= 0.4s [CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.992, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.4s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.4s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.4s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.4s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.4s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.522) total time= 0.7s [CV 4/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.546) total time= 0.6s [CV 7/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.346938775510202e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.993, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.346938775510205e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.993, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.993, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.535) total time= 0.6s [CV 10/10] END ccp_alpha=7.346938775510206e-05;, score=(train=0.992, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.993, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.535) total time= 0.6s [CV 10/10] END ccp_alpha=7.351671103223899e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.372448979591837e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.993, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.38095238095238e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.993, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.993, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.388167388167387e-05;, score=(train=0.992, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.546) total time= 0.6s [CV 7/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.992, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.431972789115647e-05;, score=(train=0.991, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.993, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.992, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.445887445887446e-05;, score=(train=0.991, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.992, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.455197132616478e-05;, score=(train=0.991, test=0.520) total time= 0.4s [CV 1/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.992, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.455453149001543e-05;, score=(train=0.991, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.512) total time= 0.4s [CV 8/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.992, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.45578231292517e-05;, score=(train=0.991, test=0.520) total time= 0.4s [CV 1/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.512) total time= 0.5s [CV 8/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.520) total time= 0.4s [CV 1/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.512) total time= 0.6s [CV 8/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.546) total time= 0.7s [CV 9/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.992, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.467532467532467e-05;, score=(train=0.991, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.991, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.990, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.990, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.992, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.518796992481207e-05;, score=(train=0.991, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.991, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.990, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.990, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.992, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.522077922077921e-05;, score=(train=0.991, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.991, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.990, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.990, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.991, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.529411764705882e-05;, score=(train=0.991, test=0.520) total time= 0.4s [CV 1/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.991, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.990, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.990, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.992, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.991, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.535321821036107e-05;, score=(train=0.991, test=0.520) total time= 0.6s [CV 1/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.521) total time= 0.6s [CV 4/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.990, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.992, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.990, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.552795031055899e-05;, score=(train=0.991, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.992, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.990, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.990, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.545) total time= 0.6s [CV 9/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.575510204081632e-05;, score=(train=0.991, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.545) total time= 0.6s [CV 9/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.992, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.990, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.598116169544751e-05;, score=(train=0.991, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.7s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 1.0s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.7s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.8s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.3s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.8s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.7s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.7s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.7s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.7s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.7s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.7s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.7s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.7s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.7s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.6s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.6s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.6s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.6s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.4s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.4s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.4s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.989, test=0.515) total time= 0.5s [CV 1/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.990, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.989, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.639419404125287e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.990, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.989, test=0.539) total time= 0.5s [CV 6/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.991, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.990, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.991, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.700447700447701e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.989, test=0.540) total time= 0.6s [CV 6/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.991, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.71004624871532e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time= 0.4s [CV 1/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.990, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=7.714285714285714e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.989, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.991, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.990, test=0.537) total time= 0.6s [CV 10/10] END ccp_alpha=7.714285714285717e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.720057720057719e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.518) total time= 0.4s [CV 4/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.989, test=0.540) total time= 0.6s [CV 6/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.991, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.548) total time= 0.6s [CV 9/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.722007722007714e-05;, score=(train=0.989, test=0.513) total time= 0.4s [CV 1/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.547) total time= 0.7s [CV 9/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.734335839598996e-05;, score=(train=0.989, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.991, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.540) total time= 0.4s [CV 6/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.990, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=7.74273345701917e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.547) total time= 0.6s [CV 9/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.747098839535807e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.540) total time= 0.5s [CV 6/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.990, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=7.757575757575755e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time= 0.4s [CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time= 0.4s [CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time= 0.4s [CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time= 0.6s [CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time= 0.6s [CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time= 0.4s [CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time= 0.5s [CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.991, test=0.523) total time= 0.4s [CV 3/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.988, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.777777777777775e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.524) total time= 0.4s [CV 2/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.989, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.988, test=0.542) total time= 0.4s [CV 6/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.549) total time= 0.6s [CV 7/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.989, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.990, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=7.811124449779912e-05;, score=(train=0.988, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.991, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.989, test=0.519) total time= 0.6s [CV 4/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.988, test=0.542) total time= 0.4s [CV 6/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.989, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.990, test=0.537) total time= 0.6s [CV 10/10] END ccp_alpha=7.822222222222224e-05;, score=(train=0.988, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.524) total time= 0.4s [CV 3/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.989, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.988, test=0.542) total time= 0.4s [CV 6/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.989, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.84313725490196e-05;, score=(train=0.988, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.524) total time= 0.6s [CV 3/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.989, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.988, test=0.542) total time= 0.6s [CV 6/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.989, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.845701209545608e-05;, score=(train=0.988, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.989, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.988, test=0.542) total time= 0.6s [CV 6/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.989, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.858503401360551e-05;, score=(train=0.988, test=0.513) total time= 0.4s [CV 1/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.989, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.988, test=0.542) total time= 0.5s [CV 6/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.988, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.989, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.860922146636434e-05;, score=(train=0.988, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.989, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.988, test=0.542) total time= 0.5s [CV 6/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.988, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.990, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.863945578231302e-05;, score=(train=0.988, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.989, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.524) total time= 0.5s [CV 3/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.989, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.988, test=0.542) total time= 0.5s [CV 6/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.988, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.990, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=7.875150060024009e-05;, score=(train=0.988, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.990, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.990, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.988, test=0.542) total time= 0.5s [CV 6/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.988, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.989, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.899159663865546e-05;, score=(train=0.988, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.988, test=0.542) total time= 0.5s [CV 6/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.988, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.989, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=7.905882352941178e-05;, score=(train=0.988, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.990, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.988, test=0.542) total time= 0.5s [CV 6/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.988, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.989, test=0.535) total time= 0.6s [CV 10/10] END ccp_alpha=7.912087912087914e-05;, score=(train=0.988, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.990, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.519) total time= 0.5s [CV 4/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.988, test=0.542) total time= 0.4s [CV 6/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.990, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.988, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.989, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=7.923809523809522e-05;, score=(train=0.988, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.520) total time= 0.5s [CV 4/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.988, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.990, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.988, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.988, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.989, test=0.538) total time= 0.6s [CV 10/10] END ccp_alpha=7.946950710108602e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.987, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.988, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.988, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.989, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=7.95283446712018e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.987, test=0.543) total time= 0.6s [CV 6/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.988, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.988, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.989, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=7.959183673469396e-05;, score=(train=0.987, test=0.512) total time= 0.4s [CV 1/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.987, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.989, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.988, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.989, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=7.981859410430838e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.989, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.987, test=0.543) total time= 0.4s [CV 6/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.989, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.989, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=7.98196406767835e-05;, score=(train=0.987, test=0.512) total time= 0.6s [CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.521) total time= 0.5s [CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.990, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.987, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.989, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.989, test=0.538) total time= 0.6s [CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.521) total time= 0.4s [CV 2/10] END ccp_alpha=8e-05;, score=(train=0.990, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.512) total time= 0.4s [CV 1/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.521) total time= 0.5s [CV 2/10] END ccp_alpha=8e-05;, score=(train=0.990, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=8e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8e-05;, score=(train=0.989, test=0.538) total time= 0.4s [CV 10/10] END ccp_alpha=8e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.541) total time= 0.6s [CV 6/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.538) total time= 0.6s [CV 10/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.511) total time= 0.4s [CV 1/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.988, test=0.538) total time= 0.6s [CV 10/10] END ccp_alpha=8.047619047619046e-05;, score=(train=0.987, test=0.511) total time= 0.5s [CV 1/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.989, test=0.526) total time= 0.6s [CV 3/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.987, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.988, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=8.060150375939859e-05;, score=(train=0.987, test=0.511) total time= 0.5s [CV 1/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.989, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.987, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.988, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=8.067005458309805e-05;, score=(train=0.987, test=0.511) total time= 0.5s [CV 1/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.989, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.989, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.989, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.987, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.988, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=8.070150045833945e-05;, score=(train=0.987, test=0.511) total time= 0.4s [CV 1/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.989, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.989, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.987, test=0.541) total time= 0.6s [CV 6/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.989, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.987, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.988, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=8.071025020177564e-05;, score=(train=0.987, test=0.511) total time= 0.4s [CV 1/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.989, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.989, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.103896103896102e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time= 0.6s [CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time= 0.4s [CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time= 0.6s [CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.525) total time= 0.6s [CV 3/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.988, test=0.537) total time= 0.6s [CV 10/10] END ccp_alpha=8.126984126984122e-05;, score=(train=0.987, test=0.512) total time= 0.5s [CV 1/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.989, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.546) total time= 0.5s [CV 9/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.126984126984125e-05;, score=(train=0.987, test=0.512) total time= 0.6s [CV 1/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.989, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.987, test=0.541) total time= 0.4s [CV 6/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.987, test=0.545) total time= 0.4s [CV 9/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.158730158730156e-05;, score=(train=0.986, test=0.512) total time= 0.6s [CV 1/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.989, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.518) total time= 0.6s [CV 4/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.989, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.986, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.987, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.988, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=8.158858685174472e-05;, score=(train=0.986, test=0.512) total time= 0.6s [CV 1/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.989, test=0.525) total time= 0.4s [CV 3/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.987, test=0.541) total time= 0.4s [CV 6/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.987, test=0.545) total time= 0.5s [CV 9/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.163265306122448e-05;, score=(train=0.986, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.989, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.989, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.989, test=0.525) total time= 0.5s [CV 3/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.517) total time= 0.4s [CV 4/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.163265306122449e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.986, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.987, test=0.548) total time= 0.6s [CV 9/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.988, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.179271708683476e-05;, score=(train=0.986, test=0.513) total time= 0.4s [CV 1/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.541) total time= 0.6s [CV 6/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.548) total time= 0.6s [CV 9/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.987, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=8.19047619047619e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.988, test=0.526) total time= 0.6s [CV 3/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.988, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.548) total time= 0.6s [CV 9/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.987, test=0.535) total time= 0.5s [CV 10/10] END ccp_alpha=8.198757763975154e-05;, score=(train=0.986, test=0.513) total time= 0.4s [CV 1/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.521) total time= 0.5s [CV 2/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.988, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.988, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.986, test=0.541) total time= 0.6s [CV 6/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.548) total time= 0.6s [CV 9/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.987, test=0.535) total time= 0.6s [CV 10/10] END ccp_alpha=8.220781645991727e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.541) total time= 0.6s [CV 6/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.526) total time= 0.6s [CV 3/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.231292517006807e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.988, test=0.526) total time= 0.5s [CV 3/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.986, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.548) total time= 0.4s [CV 9/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.987, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=8.235294117647061e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.988, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.986, test=0.541) total time= 0.4s [CV 6/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.987, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=8.260566580821085e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.988, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.986, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.987, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=8.264126984126985e-05;, score=(train=0.986, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.988, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.986, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.26530612244898e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 1/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.988, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.986, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.987, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.277077054199345e-05;, score=(train=0.986, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.988, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 5/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.986, test=0.541) total time= 0.5s [CV 6/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.988, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.987, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=8.277837450769786e-05;, score=(train=0.986, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.988, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.986, test=0.542) total time= 0.4s [CV 6/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.988, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.296296296296295e-05;, score=(train=0.986, test=0.513) total time= 0.6s [CV 1/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.988, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.988, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.517) total time= 0.4s [CV 4/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.31168831168831e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time= 0.6s [CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time= 0.6s [CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time= 0.4s [CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.988, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.311688311688311e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 1/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.988, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.986, test=0.544) total time= 0.5s [CV 6/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.333333333333334e-05;, score=(train=0.986, test=0.514) total time= 0.6s [CV 1/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.986, test=0.544) total time= 0.5s [CV 6/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.986, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.987, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=8.34467120181406e-05;, score=(train=0.985, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 5/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.544) total time= 0.5s [CV 6/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.986, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.987, test=0.536) total time= 0.4s [CV 10/10] END ccp_alpha=8.359183673469384e-05;, score=(train=0.985, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 5/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.543) total time= 0.4s [CV 6/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.985, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.548) total time= 0.5s [CV 9/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.985, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.518) total time= 0.5s [CV 4/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.986, test=0.548) total time= 0.6s [CV 9/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.987, test=0.536) total time= 0.5s [CV 10/10] END ccp_alpha=8.38095238095238e-05;, score=(train=0.985, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.543) total time= 0.6s [CV 6/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.397515527950304e-05;, score=(train=0.985, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.398617511520741e-05;, score=(train=0.985, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.987, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.401360544217687e-05;, score=(train=0.985, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.543) total time= 0.4s [CV 6/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 5/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.986, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=8.40336134453782e-05;, score=(train=0.985, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 5/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.412698412698413e-05;, score=(train=0.985, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.6s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.6s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.6s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.4s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.6s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.4s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.4s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.6s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.523) total time= 0.6s [CV 2/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.986, test=0.537) total time= 0.5s [CV 10/10] END ccp_alpha=8.415584415584413e-05;, score=(train=0.985, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.517) total time= 0.6s [CV 4/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.985, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.538) total time= 0.5s [CV 10/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.984, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.517) total time= 0.5s [CV 4/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.543) total time= 0.5s [CV 6/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.987, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.985, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.547) total time= 0.5s [CV 9/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.986, test=0.538) total time= 0.6s [CV 10/10] END ccp_alpha=8.439560439560439e-05;, score=(train=0.984, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.982, test=0.521) total time= 0.5s [CV 4/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.982, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.465608465608466e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.982, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.465608465608467e-05;, score=(train=0.980, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.982, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.465608465608472e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.984, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.982, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.46886446886447e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.982, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.470328842999649e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.982, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.489795918367346e-05;, score=(train=0.980, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.984, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.982, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.489795918367347e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.982, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.982, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.49266022198353e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.982, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.501253132832077e-05;, score=(train=0.980, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.980, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.507936507936504e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.546) total time= 0.6s [CV 7/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.980, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.981, test=0.541) total time= 0.4s [CV 10/10] END ccp_alpha=8.513591671486412e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.980, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.981, test=0.541) total time= 0.5s [CV 10/10] END ccp_alpha=8.52706299911269e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.981, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.539682539682539e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.981, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.55328798185941e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.546) total time= 0.6s [CV 7/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.981, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.554720133667503e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.984, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.981, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.57142857142857e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.6s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.4s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.980, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.979, test=0.516) total time= 0.5s [CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.983, test=0.529) total time= 0.5s [CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.979, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.981, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.983, test=0.529) total time= 0.6s [CV 3/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.593073593073593e-05;, score=(train=0.979, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.981, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.983, test=0.529) total time= 0.4s [CV 3/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.532) total time= 0.6s [CV 5/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.593350383631724e-05;, score=(train=0.979, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.981, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.983, test=0.529) total time= 0.5s [CV 3/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.981, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.981, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.598639455782311e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.981, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.983, test=0.529) total time= 0.5s [CV 3/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.981, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.6002886002886e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.981, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.983, test=0.529) total time= 0.5s [CV 3/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.981, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.980, test=0.543) total time= 0.6s [CV 10/10] END ccp_alpha=8.622448979591836e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.981, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.983, test=0.529) total time= 0.5s [CV 3/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.981, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.980, test=0.543) total time= 0.6s [CV 10/10] END ccp_alpha=8.634920634920634e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time= 0.4s [CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time= 0.6s [CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time= 0.4s [CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.981, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.658008658008658e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.983, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.979, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.662131519274373e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.979, test=0.551) total time= 0.6s [CV 9/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.980, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.664799253034547e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.979, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=8.669387755102044e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.979, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=8.669467787114846e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.533) total time= 0.6s [CV 5/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.685714285714286e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.982, test=0.527) total time= 0.6s [CV 3/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.533) total time= 0.6s [CV 5/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.687074829931974e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.690476190476186e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.69172932330827e-05;, score=(train=0.978, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.979, test=0.546) total time= 0.4s [CV 10/10] END ccp_alpha=8.707482993197276e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.707482993197278e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.6s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.4s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.70748299319728e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.980, test=0.529) total time= 0.5s [CV 2/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.715424925951244e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.980, test=0.529) total time= 0.5s [CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.551) total time= 0.6s [CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.978, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.980, test=0.529) total time= 0.5s [CV 2/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.980, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.551) total time= 0.4s [CV 6/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.980, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.978, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.72727272727273e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.980, test=0.529) total time= 0.6s [CV 2/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.980, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.978, test=0.551) total time= 0.4s [CV 9/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.979, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=8.728106988976545e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.980, test=0.529) total time= 0.5s [CV 2/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.980, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.978, test=0.551) total time= 0.5s [CV 9/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.979, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=8.734968734968733e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.982, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.980, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.980, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.978, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.774512737646377e-05;, score=(train=0.978, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.982, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.533) total time= 0.6s [CV 5/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.980, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.977, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.784313725490196e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.982, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.979, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.980, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.79104635202196e-05;, score=(train=0.978, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.982, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.982, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.982, test=0.528) total time= 0.4s [CV 3/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.533) total time= 0.6s [CV 5/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.979, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.791208791208792e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.982, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.979, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.792517006802718e-05;, score=(train=0.978, test=0.520) total time= 0.6s [CV 1/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.982, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.526) total time= 0.4s [CV 4/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.977, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.544) total time= 0.6s [CV 10/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.982, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.979, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.796622097114707e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.982, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.979, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.977, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.798185941043082e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.982, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.979, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.80952380952381e-05;, score=(train=0.978, test=0.520) total time= 0.6s [CV 1/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.982, test=0.528) total time= 0.5s [CV 3/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.533) total time= 0.6s [CV 5/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.979, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.977, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.544) total time= 0.6s [CV 10/10] END ccp_alpha=8.811013547855655e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.982, test=0.528) total time= 0.6s [CV 3/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.979, test=0.519) total time= 0.6s [CV 8/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.811188811188805e-05;, score=(train=0.978, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.978, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.977, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.829131652661064e-05;, score=(train=0.977, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.982, test=0.527) total time= 0.6s [CV 3/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.838095238095237e-05;, score=(train=0.977, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.841991341991339e-05;, score=(train=0.977, test=0.520) total time= 0.5s [CV 1/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.979, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.842105263157899e-05;, score=(train=0.977, test=0.520) total time= 0.4s [CV 1/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.978, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.978, test=0.544) total time= 0.6s [CV 10/10] END ccp_alpha=8.845550905351557e-05;, score=(train=0.977, test=0.520) total time= 0.4s [CV 1/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.979, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.85850991114149e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.978, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.978, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.979, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.977, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.978, test=0.544) total time= 0.6s [CV 10/10] END ccp_alpha=8.860544217687073e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time= 0.5s [CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.982, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.864468864468862e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.982, test=0.527) total time= 0.6s [CV 3/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.979, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.979, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.979, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.978, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=8.874483429219683e-05;, score=(train=0.977, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.982, test=0.527) total time= 0.4s [CV 3/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.979, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.532) total time= 0.6s [CV 5/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.979, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.977, test=0.552) total time= 0.6s [CV 9/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.978, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=8.877551020408162e-05;, score=(train=0.977, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.981, test=0.527) total time= 0.6s [CV 3/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.978, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.979, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.977, test=0.543) total time= 0.4s [CV 10/10] END ccp_alpha=8.888888888888884e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.981, test=0.527) total time= 0.5s [CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.532) total time= 0.5s [CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.978, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.979, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.977, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.978, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 9/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.976, test=0.543) total time= 0.6s [CV 10/10] END ccp_alpha=8.898785108793321e-05;, score=(train=0.977, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.977, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.981, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.978, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.979, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.977, test=0.552) total time= 0.4s [CV 9/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.976, test=0.543) total time= 0.4s [CV 10/10] END ccp_alpha=8.89955982392957e-05;, score=(train=0.977, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.977, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.978, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.977, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.978, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.978, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.976, test=0.553) total time= 0.4s [CV 9/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.908187388430548e-05;, score=(train=0.976, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.977, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.978, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.977, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.977, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.978, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.976, test=0.553) total time= 0.6s [CV 9/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.92517006802721e-05;, score=(train=0.976, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.981, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.977, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.978, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.553) total time= 0.6s [CV 9/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.92857142857143e-05;, score=(train=0.976, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.977, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.978, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.978, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.979, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.976, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.976, test=0.543) total time= 0.4s [CV 10/10] END ccp_alpha=8.930117501546072e-05;, score=(train=0.976, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.977, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.978, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.977, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.978, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.978, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.976, test=0.553) total time= 0.6s [CV 9/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.933444722918406e-05;, score=(train=0.976, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.977, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.978, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.977, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.978, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.976, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.936170212765944e-05;, score=(train=0.976, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.981, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.978, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.977, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.978, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.976, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.941798941798949e-05;, score=(train=0.976, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.980, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.977, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.978, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.975, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.979591836734696e-05;, score=(train=0.976, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.980, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.977, test=0.547) total time= 0.6s [CV 7/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.991662469923337e-05;, score=(train=0.976, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.980, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.976, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.977, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.978, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.975, test=0.554) total time= 0.6s [CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.976, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.980, test=0.531) total time= 0.6s [CV 3/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.976, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.977, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.978, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=9.010025062656638e-05;, score=(train=0.976, test=0.518) total time= 0.6s [CV 1/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.980, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.977, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.978, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=9.017150719937095e-05;, score=(train=0.976, test=0.518) total time= 0.6s [CV 1/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.980, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.977, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.978, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.976, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=9.017169557102065e-05;, score=(train=0.976, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.977, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.980, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.976, test=0.551) total time= 0.5s [CV 6/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.977, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.978, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.975, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=9.021645021645021e-05;, score=(train=0.976, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.976, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.980, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.977, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.032526671870936e-05;, score=(train=0.975, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.976, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.980, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.977, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.976, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.977, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.975, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.0344438170525e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.980, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.977, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.038961038961032e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.527) total time= 0.4s [CV 2/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.976, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.977, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.042386185243336e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.526) total time= 0.6s [CV 2/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.977, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.047619047619046e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time= 0.4s [CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time= 0.6s [CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.526) total time= 0.4s [CV 2/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.977, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.047619047619049e-05;, score=(train=0.975, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.980, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.977, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.976, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.977, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.975, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.04761904761905e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.980, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.976, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.977, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.975, test=0.554) total time= 0.6s [CV 9/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.975, test=0.542) total time= 0.5s [CV 10/10] END ccp_alpha=9.061858776144487e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.527) total time= 0.5s [CV 2/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.980, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.976, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.977, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.974, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.975, test=0.543) total time= 0.5s [CV 10/10] END ccp_alpha=9.070294784580498e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.976, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.979, test=0.532) total time= 0.4s [CV 3/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.976, test=0.524) total time= 0.4s [CV 4/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.975, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.976, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.977, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.974, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.974, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=9.071428571428571e-05;, score=(train=0.974, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.976, test=0.526) total time= 0.5s [CV 2/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 3/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.975, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.975, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.976, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.976, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.974, test=0.554) total time= 0.6s [CV 9/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.974, test=0.544) total time= 0.4s [CV 10/10] END ccp_alpha=9.081289081289083e-05;, score=(train=0.974, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.979, test=0.532) total time= 0.5s [CV 3/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.976, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.976, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.974, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.974, test=0.544) total time= 0.5s [CV 10/10] END ccp_alpha=9.097505668934239e-05;, score=(train=0.974, test=0.520) total time= 0.4s [CV 1/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.975, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.976, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.973, test=0.553) total time= 0.4s [CV 9/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.119047619047616e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.975, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.976, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.973, test=0.553) total time= 0.6s [CV 9/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.122422915526365e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.130434782608698e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.974, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.973, test=0.554) total time= 0.6s [CV 9/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.137794652744818e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857135e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.6s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.6s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.6s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.6s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.4s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.7s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.4s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.4s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.4s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.4s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.4s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.4s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.6s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.976, test=0.520) total time= 0.6s [CV 8/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.973, test=0.545) total time= 0.5s [CV 10/10] END ccp_alpha=9.14285714285714e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.974, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.973, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.972, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857143e-05;, score=(train=0.973, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.978, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.553) total time= 0.6s [CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.976, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.972, test=0.555) total time= 0.4s [CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.978, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.976, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.972, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.973, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.972, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.183673469387755e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.972, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.973, test=0.546) total time= 0.4s [CV 10/10] END ccp_alpha=9.185185185185186e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time= 0.4s [CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time= 0.6s [CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time= 0.6s [CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time= 0.6s [CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.545) total time= 0.4s [CV 7/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.975, test=0.519) total time= 0.6s [CV 8/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.972, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.197278911564626e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.545) total time= 0.6s [CV 7/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.972, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.199444233926995e-05;, score=(train=0.973, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.972, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.201049165952777e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.978, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.974, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.545) total time= 0.5s [CV 7/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.975, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.973, test=0.546) total time= 0.4s [CV 10/10] END ccp_alpha=9.206349206349203e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.973, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.975, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.216525634644103e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.975, test=0.525) total time= 0.6s [CV 2/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.973, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.975, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.975, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.218794494542007e-05;, score=(train=0.973, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time= 0.6s [CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time= 0.6s [CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time= 0.4s [CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time= 0.4s [CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.525) total time= 0.5s [CV 2/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.978, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.975, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 9/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.230769230769234e-05;, score=(train=0.973, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.524) total time= 0.5s [CV 2/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.973, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.546) total time= 0.4s [CV 7/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.974, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.971, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.972, test=0.546) total time= 0.5s [CV 10/10] END ccp_alpha=9.238095238095239e-05;, score=(train=0.972, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.970, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.972, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.971, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.552) total time= 0.4s [CV 6/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.970, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.972, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.971, test=0.518) total time= 0.4s [CV 1/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.973, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.546) total time= 0.6s [CV 7/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.974, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.970, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.972, test=0.547) total time= 0.4s [CV 10/10] END ccp_alpha=9.285714285714286e-05;, score=(train=0.971, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.977, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.973, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.974, test=0.552) total time= 0.7s [CV 6/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.973, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.970, test=0.557) total time= 0.4s [CV 9/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.972, test=0.548) total time= 0.4s [CV 10/10] END ccp_alpha=9.297168171567627e-05;, score=(train=0.971, test=0.518) total time= 0.6s [CV 1/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.977, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.974, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.973, test=0.530) total time= 0.6s [CV 5/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.974, test=0.546) total time= 0.5s [CV 7/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.973, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.970, test=0.557) total time= 0.5s [CV 9/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.972, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.301587301587303e-05;, score=(train=0.971, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time= 0.4s [CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time= 0.6s [CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time= 0.4s [CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time= 0.4s [CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.523) total time= 0.6s [CV 2/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.974, test=0.552) total time= 0.6s [CV 6/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.969, test=0.556) total time= 0.4s [CV 9/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.547) total time= 0.4s [CV 10/10] END ccp_alpha=9.333333333333332e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.973, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.972, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.969, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.971, test=0.547) total time= 0.4s [CV 10/10] END ccp_alpha=9.333333333333337e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.973, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.973, test=0.522) total time= 0.6s [CV 4/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.969, test=0.556) total time= 0.5s [CV 9/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.971, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.33333333333334e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.973, test=0.522) total time= 0.5s [CV 4/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.974, test=0.552) total time= 0.5s [CV 6/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.973, test=0.547) total time= 0.5s [CV 7/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.972, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.969, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.971, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.344236010902676e-05;, score=(train=0.971, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time= 0.6s [CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time= 0.4s [CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time= 0.6s [CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time= 0.6s [CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time= 0.6s [CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time= 0.4s [CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time= 0.6s [CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time= 0.6s [CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time= 0.6s [CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.976, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.972, test=0.518) total time= 0.5s [CV 8/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.969, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.971, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.35064935064935e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.976, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.973, test=0.524) total time= 0.6s [CV 4/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.968, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.970, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.37728937728938e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.972, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.976, test=0.530) total time= 0.6s [CV 3/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.968, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.972, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.976, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.968, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.548) total time= 0.6s [CV 10/10] END ccp_alpha=9.377289377289383e-05;, score=(train=0.970, test=0.518) total time= 0.6s [CV 1/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.976, test=0.530) total time= 0.5s [CV 3/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.973, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.973, test=0.553) total time= 0.6s [CV 6/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.968, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.970, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.379292386811188e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.976, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.973, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.971, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.968, test=0.555) total time= 0.5s [CV 9/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.970, test=0.548) total time= 0.6s [CV 10/10] END ccp_alpha=9.382239382239381e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.976, test=0.530) total time= 0.4s [CV 3/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.973, test=0.524) total time= 0.5s [CV 4/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.973, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.971, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.968, test=0.555) total time= 0.6s [CV 9/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.970, test=0.548) total time= 0.4s [CV 10/10] END ccp_alpha=9.396146082802752e-05;, score=(train=0.970, test=0.518) total time= 0.5s [CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.553) total time= 0.4s [CV 6/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.973, test=0.548) total time= 0.6s [CV 7/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.971, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.970, test=0.549) total time= 0.6s [CV 10/10] END ccp_alpha=9.411764705882353e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.972, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.975, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.973, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.971, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.968, test=0.554) total time= 0.6s [CV 9/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.970, test=0.549) total time= 0.5s [CV 10/10] END ccp_alpha=9.413756613756614e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.975, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.971, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.973, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.971, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.968, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.970, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.419913419913421e-05;, score=(train=0.969, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.972, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.973, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.973, test=0.548) total time= 0.4s [CV 7/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.971, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.970, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.42469295410472e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.975, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.553) total time= 0.6s [CV 6/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.972, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.971, test=0.516) total time= 0.4s [CV 8/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.970, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.428571428571426e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.522) total time= 0.6s [CV 2/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.970, test=0.548) total time= 0.5s [CV 10/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.969, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.968, test=0.554) total time= 0.4s [CV 9/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.970, test=0.548) total time= 0.4s [CV 10/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.975, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.529) total time= 0.5s [CV 5/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.972, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.971, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.970, test=0.548) total time= 0.6s [CV 10/10] END ccp_alpha=9.428571428571429e-05;, score=(train=0.969, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.971, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.975, test=0.532) total time= 0.5s [CV 3/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.972, test=0.523) total time= 0.5s [CV 4/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.971, test=0.529) total time= 0.6s [CV 5/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.972, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.970, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.969, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.450549450549451e-05;, score=(train=0.969, test=0.519) total time= 0.6s [CV 1/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.971, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.975, test=0.532) total time= 0.5s [CV 3/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.523) total time= 0.6s [CV 4/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.553) total time= 0.6s [CV 6/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.519) total time= 0.5s [CV 1/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.971, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.975, test=0.532) total time= 0.6s [CV 3/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.523) total time= 0.4s [CV 4/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.553) total time= 0.5s [CV 6/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.972, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.970, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.45378151260504e-05;, score=(train=0.969, test=0.519) total time= 0.4s [CV 1/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.971, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.974, test=0.531) total time= 0.5s [CV 3/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.972, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.971, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.972, test=0.548) total time= 0.5s [CV 7/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.547) total time= 0.4s [CV 10/10] END ccp_alpha=9.487179487179487e-05;, score=(train=0.969, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.971, test=0.523) total time= 0.6s [CV 2/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.974, test=0.531) total time= 0.4s [CV 3/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.972, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.971, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.972, test=0.549) total time= 0.6s [CV 7/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.494505494505516e-05;, score=(train=0.969, test=0.517) total time= 0.6s [CV 1/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.971, test=0.523) total time= 0.5s [CV 2/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.974, test=0.532) total time= 0.5s [CV 3/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.972, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.530) total time= 0.5s [CV 5/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.971, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.972, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.968, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.499072356215215e-05;, score=(train=0.969, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.971, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.974, test=0.532) total time= 0.4s [CV 3/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.972, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.531) total time= 0.6s [CV 5/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.971, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.972, test=0.549) total time= 0.6s [CV 7/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.547) total time= 0.6s [CV 10/10] END ccp_alpha=9.517460317460317e-05;, score=(train=0.969, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.522) total time= 0.4s [CV 2/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.974, test=0.532) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.549) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.517) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.522) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.974, test=0.532) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.531) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.971, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.972, test=0.549) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.547) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809523e-05;, score=(train=0.969, test=0.517) total time= 0.4s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.6s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.6s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.4s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.6s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.6s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.4s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.6s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.4s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.6s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.6s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.6s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.6s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.4s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.6s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.6s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.4s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.4s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.6s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.6s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.6s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.6s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.969, test=0.535) total time= 0.6s [CV 3/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.525) total time= 0.5s [CV 4/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.967, test=0.554) total time= 0.6s [CV 7/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.963, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.523809523809524e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.965, test=0.528) total time= 0.6s [CV 2/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.967, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.963, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.966, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.967, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.962, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.962, test=0.552) total time= 0.5s [CV 10/10] END ccp_alpha=9.541301076184793e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.967, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.963, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.966, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.967, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.962, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.962, test=0.552) total time= 0.4s [CV 10/10] END ccp_alpha=9.54545454545454e-05;, score=(train=0.964, test=0.521) total time= 0.6s [CV 1/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.965, test=0.528) total time= 0.4s [CV 2/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.963, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.966, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.552) total time= 0.5s [CV 10/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.963, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.966, test=0.554) total time= 0.4s [CV 6/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.967, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.962, test=0.552) total time= 0.5s [CV 10/10] END ccp_alpha=9.54621848739496e-05;, score=(train=0.964, test=0.521) total time= 0.4s [CV 1/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.965, test=0.528) total time= 0.5s [CV 2/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.967, test=0.526) total time= 0.5s [CV 4/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.963, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.966, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.967, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.964, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.962, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.962, test=0.552) total time= 0.5s [CV 10/10] END ccp_alpha=9.547471162377983e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.529) total time= 0.5s [CV 2/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.969, test=0.535) total time= 0.4s [CV 3/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.527) total time= 0.5s [CV 4/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.967, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.552) total time= 0.5s [CV 10/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.529) total time= 0.5s [CV 2/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.527) total time= 0.6s [CV 4/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.966, test=0.555) total time= 0.4s [CV 6/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.967, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.963, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.962, test=0.552) total time= 0.5s [CV 10/10] END ccp_alpha=9.561157796451912e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.964, test=0.529) total time= 0.5s [CV 2/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.969, test=0.535) total time= 0.5s [CV 3/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.966, test=0.527) total time= 0.6s [CV 4/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.963, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.966, test=0.555) total time= 0.4s [CV 6/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.967, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.963, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.962, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.962, test=0.552) total time= 0.5s [CV 10/10] END ccp_alpha=9.568786831944722e-05;, score=(train=0.964, test=0.521) total time= 0.5s [CV 1/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.963, test=0.532) total time= 0.5s [CV 2/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.964, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.962, test=0.524) total time= 0.4s [CV 5/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.965, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.966, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.962, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.961, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.961, test=0.554) total time= 0.4s [CV 10/10] END ccp_alpha=9.62406015037594e-05;, score=(train=0.963, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.963, test=0.532) total time= 0.6s [CV 2/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.962, test=0.525) total time= 0.4s [CV 5/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.965, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.965, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.962, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.960, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.961, test=0.553) total time= 0.4s [CV 10/10] END ccp_alpha=9.634408602150535e-05;, score=(train=0.963, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.963, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.965, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.964, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.962, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.650706436420707e-05;, score=(train=0.963, test=0.522) total time= 0.6s [CV 1/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.963, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.965, test=0.555) total time= 0.6s [CV 6/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.962, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.960, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.65079365079365e-05;, score=(train=0.963, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.525) total time= 0.6s [CV 5/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.964, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.656607408900979e-05;, score=(train=0.962, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.556) total time= 0.4s [CV 6/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.515) total time= 0.4s [CV 8/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.657477025898073e-05;, score=(train=0.962, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.964, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.961, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.670108579944647e-05;, score=(train=0.962, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time= 0.4s [CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time= 0.6s [CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time= 0.6s [CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time= 0.4s [CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time= 0.4s [CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time= 0.6s [CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time= 0.6s [CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time= 0.6s [CV 1/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 2/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.968, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.556) total time= 0.6s [CV 6/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.964, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.960, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.961, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.67032967032967e-05;, score=(train=0.962, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.962, test=0.533) total time= 0.5s [CV 2/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.967, test=0.536) total time= 0.4s [CV 3/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.963, test=0.530) total time= 0.5s [CV 4/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.961, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.963, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.963, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.960, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.959, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.960, test=0.552) total time= 0.6s [CV 10/10] END ccp_alpha=9.678017220874362e-05;, score=(train=0.961, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.962, test=0.533) total time= 0.5s [CV 2/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.967, test=0.536) total time= 0.5s [CV 3/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.963, test=0.530) total time= 0.5s [CV 4/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.961, test=0.525) total time= 0.6s [CV 5/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.963, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.963, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.960, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.959, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.960, test=0.552) total time= 0.6s [CV 10/10] END ccp_alpha=9.679714251142822e-05;, score=(train=0.961, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.962, test=0.533) total time= 0.5s [CV 2/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.966, test=0.537) total time= 0.5s [CV 3/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.530) total time= 0.5s [CV 4/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.554) total time= 0.5s [CV 7/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.959, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.962, test=0.533) total time= 0.5s [CV 2/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.966, test=0.537) total time= 0.6s [CV 3/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.530) total time= 0.6s [CV 4/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.963, test=0.554) total time= 0.4s [CV 7/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.959, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.960, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.688644688644695e-05;, score=(train=0.961, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.962, test=0.533) total time= 0.4s [CV 2/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.966, test=0.537) total time= 0.4s [CV 3/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.963, test=0.530) total time= 0.5s [CV 4/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.961, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.963, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.963, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.960, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.959, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.960, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.694825298540463e-05;, score=(train=0.961, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.962, test=0.533) total time= 0.5s [CV 2/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.966, test=0.537) total time= 0.5s [CV 3/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.963, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.960, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.963, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.963, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.960, test=0.514) total time= 0.6s [CV 8/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.959, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.959, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.714285714285712e-05;, score=(train=0.961, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.965, test=0.538) total time= 0.6s [CV 3/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.960, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.962, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.963, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.960, test=0.515) total time= 0.5s [CV 8/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.959, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.959, test=0.551) total time= 0.5s [CV 10/10] END ccp_alpha=9.746031746031747e-05;, score=(train=0.961, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.960, test=0.534) total time= 0.6s [CV 2/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.961, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.962, test=0.555) total time= 0.6s [CV 7/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.959, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346938e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.6s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.4s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.6s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.4s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.4s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.6s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.6s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.6s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.6s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.6s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.4s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.6s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.6s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.4s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.4s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.4s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.6s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.4s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.6s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.6s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.6s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.4s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.965, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.961, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.962, test=0.555) total time= 0.6s [CV 7/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.958, test=0.554) total time= 0.4s [CV 10/10] END ccp_alpha=9.795918367346941e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.960, test=0.534) total time= 0.6s [CV 2/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.965, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.962, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.961, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.962, test=0.555) total time= 0.6s [CV 7/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.959, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.958, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.795918367346947e-05;, score=(train=0.959, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.960, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.964, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.962, test=0.532) total time= 0.5s [CV 4/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.961, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.962, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.958, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.958, test=0.558) total time= 0.4s [CV 9/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.958, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.811616954474097e-05;, score=(train=0.959, test=0.524) total time= 0.6s [CV 1/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.959, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.964, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.962, test=0.531) total time= 0.4s [CV 4/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.961, test=0.557) total time= 0.6s [CV 6/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.961, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.958, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.958, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.958, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.818181818181822e-05;, score=(train=0.959, test=0.524) total time= 0.6s [CV 1/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.959, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.964, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.961, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.959, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.961, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.961, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.958, test=0.513) total time= 0.5s [CV 8/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.957, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.958, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.823129251700683e-05;, score=(train=0.959, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.959, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.964, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.961, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.959, test=0.528) total time= 0.6s [CV 5/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.961, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.961, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.958, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.957, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.958, test=0.553) total time= 0.6s [CV 10/10] END ccp_alpha=9.828571428571423e-05;, score=(train=0.959, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.959, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.964, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.961, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.959, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.961, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.961, test=0.555) total time= 0.5s [CV 7/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.958, test=0.514) total time= 0.4s [CV 8/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.957, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.958, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.840283939662823e-05;, score=(train=0.959, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.959, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.964, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.961, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.959, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.961, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.961, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.958, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.957, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.958, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.84103986817475e-05;, score=(train=0.959, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.959, test=0.534) total time= 0.5s [CV 2/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.964, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.961, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.959, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.960, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.961, test=0.555) total time= 0.4s [CV 7/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.958, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.957, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.957, test=0.553) total time= 0.5s [CV 10/10] END ccp_alpha=9.849168630270068e-05;, score=(train=0.958, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.958, test=0.533) total time= 0.5s [CV 2/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.964, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.961, test=0.532) total time= 0.6s [CV 4/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.958, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.960, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.960, test=0.556) total time= 0.5s [CV 7/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.957, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.956, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.956, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.870129870129872e-05;, score=(train=0.958, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.958, test=0.533) total time= 0.4s [CV 2/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.964, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.961, test=0.532) total time= 0.6s [CV 4/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.958, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.960, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.960, test=0.556) total time= 0.5s [CV 7/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.957, test=0.514) total time= 0.5s [CV 8/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.956, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.956, test=0.554) total time= 0.5s [CV 10/10] END ccp_alpha=9.875012205839262e-05;, score=(train=0.958, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.957, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.963, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.960, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.957, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.959, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.959, test=0.556) total time= 0.5s [CV 7/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.956, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.955, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.955, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=9.890109890109892e-05;, score=(train=0.957, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.957, test=0.536) total time= 0.4s [CV 2/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.963, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.960, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.957, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.959, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.959, test=0.556) total time= 0.5s [CV 7/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.956, test=0.516) total time= 0.6s [CV 8/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.955, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=9.911816578483244e-05;, score=(train=0.957, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.957, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.963, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.960, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.956, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.959, test=0.558) total time= 0.4s [CV 6/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.959, test=0.556) total time= 0.5s [CV 7/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.956, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.955, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=9.918367346938782e-05;, score=(train=0.957, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.957, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.963, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.960, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.956, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.959, test=0.557) total time= 0.4s [CV 6/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.959, test=0.556) total time= 0.5s [CV 7/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.955, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.954, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=9.929166927850563e-05;, score=(train=0.956, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.957, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.962, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.960, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.956, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.958, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.959, test=0.556) total time= 0.6s [CV 7/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.955, test=0.516) total time= 0.4s [CV 8/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.954, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.954, test=0.556) total time= 0.4s [CV 10/10] END ccp_alpha=9.939673982800666e-05;, score=(train=0.956, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.957, test=0.536) total time= 0.6s [CV 2/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.962, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.960, test=0.529) total time= 0.5s [CV 4/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.956, test=0.528) total time= 0.5s [CV 5/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.958, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.959, test=0.556) total time= 0.6s [CV 7/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.955, test=0.516) total time= 0.5s [CV 8/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.954, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.954, test=0.556) total time= 0.4s [CV 10/10] END ccp_alpha=9.942857142857144e-05;, score=(train=0.956, test=0.525) total time= 0.5s [CV 1/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.957, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.962, test=0.540) total time= 0.5s [CV 3/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.959, test=0.530) total time= 0.5s [CV 4/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.956, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.958, test=0.556) total time= 0.6s [CV 6/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.958, test=0.556) total time= 0.5s [CV 7/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.955, test=0.516) total time= 0.4s [CV 8/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.954, test=0.558) total time= 0.5s [CV 9/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=9.974025974025975e-05;, score=(train=0.956, test=0.522) total time= 0.6s [CV 1/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.957, test=0.554) total time= 0.5s [CV 6/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.958, test=0.557) total time= 0.4s [CV 7/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=9.999999999999998e-05;, score=(train=0.956, test=0.522) total time= 0.6s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.6s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.6s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.6s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.6s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.6s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.6s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.536) total time= 0.6s [CV 2/10] END ccp_alpha=0.0001;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001;, score=(train=0.959, test=0.531) total time= 0.6s [CV 4/10] END ccp_alpha=0.0001;, score=(train=0.955, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001;, score=(train=0.958, test=0.557) total time= 0.6s [CV 7/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001;, score=(train=0.954, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.959, test=0.531) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.955, test=0.526) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.958, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.953, test=0.559) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.954, test=0.556) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010000000000000002;, score=(train=0.956, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.955, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.961, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.959, test=0.530) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.954, test=0.525) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.956, test=0.555) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.957, test=0.557) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.954, test=0.517) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.952, test=0.560) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.952, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010033444816053512;, score=(train=0.956, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.960, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.958, test=0.532) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.953, test=0.525) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.955, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.956, test=0.558) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.954, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.951, test=0.561) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.951, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010062602853159753;, score=(train=0.955, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.960, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.958, test=0.532) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.953, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.955, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.956, test=0.558) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.954, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.951, test=0.561) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.951, test=0.556) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010063492063492065;, score=(train=0.955, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.960, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.957, test=0.534) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.953, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.955, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.956, test=0.558) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.954, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.951, test=0.561) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.951, test=0.557) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010071859729807414;, score=(train=0.955, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.959, test=0.542) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.957, test=0.533) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.953, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.955, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.956, test=0.558) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.953, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.951, test=0.561) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.951, test=0.557) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010084033613445377;, score=(train=0.955, test=0.524) total time= 0.6s [CV 1/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.959, test=0.542) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.957, test=0.533) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.953, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.955, test=0.556) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.956, test=0.558) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.953, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.951, test=0.561) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.951, test=0.557) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010086580086580086;, score=(train=0.955, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.959, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.957, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.953, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.955, test=0.557) total time= 0.6s [CV 6/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.956, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.953, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.951, test=0.561) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.951, test=0.557) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010104308390022676;, score=(train=0.955, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.959, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.956, test=0.533) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.952, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.954, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.956, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.953, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.951, test=0.561) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.951, test=0.557) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010117566643882431;, score=(train=0.954, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.959, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.956, test=0.533) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.952, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.954, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.956, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.953, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.950, test=0.562) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.951, test=0.557) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010127472527472523;, score=(train=0.954, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.959, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.956, test=0.533) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.952, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.954, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.955, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.953, test=0.519) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.950, test=0.562) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.950, test=0.558) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010133333333333338;, score=(train=0.954, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.959, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.956, test=0.533) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.952, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.954, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.955, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.952, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.950, test=0.562) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.950, test=0.558) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010149787614576345;, score=(train=0.954, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.954, test=0.535) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.959, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.956, test=0.533) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.952, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.954, test=0.557) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.955, test=0.560) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.952, test=0.519) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.950, test=0.561) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.950, test=0.558) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010153942167672147;, score=(train=0.954, test=0.524) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.950, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.526) total time= 0.6s [CV 5/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.520) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.946, test=0.563) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.950, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.948, test=0.520) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.946, test=0.563) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010158730158730156;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.6s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.6s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.6s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.536) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.950, test=0.558) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.945, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.946, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730157;, score=(train=0.949, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.6s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.6s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.6s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.6s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.6s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.6s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.6s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.6s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010158730158730159;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.948, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.950, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010160864345738295;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.955, test=0.539) total time= 0.6s [CV 3/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.948, test=0.525) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.950, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.945, test=0.563) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010168067226890747;, score=(train=0.949, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.955, test=0.539) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.950, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.951, test=0.559) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.948, test=0.521) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010172684458398739;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.949, test=0.537) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.955, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.951, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.948, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.950, test=0.560) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.951, test=0.559) total time= 0.6s [CV 7/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.947, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.944, test=0.567) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010179894179894179;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.947, test=0.538) total time= 0.6s [CV 2/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.950, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.947, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.949, test=0.561) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.950, test=0.561) total time= 0.6s [CV 7/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.947, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.943, test=0.566) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010203946775279887;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.947, test=0.538) total time= 0.6s [CV 2/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.950, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.947, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.949, test=0.561) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.950, test=0.561) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.947, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.943, test=0.566) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010204081632653053;, score=(train=0.949, test=0.522) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.947, test=0.538) total time= 0.6s [CV 2/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.954, test=0.541) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.950, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.947, test=0.526) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.949, test=0.561) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.950, test=0.561) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.947, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.943, test=0.566) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010204081632653059;, score=(train=0.949, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.950, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.947, test=0.525) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.949, test=0.561) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.950, test=0.561) total time= 0.6s [CV 7/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.947, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.943, test=0.566) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.945, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010208821593153387;, score=(train=0.948, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.947, test=0.538) total time= 0.6s [CV 2/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.954, test=0.541) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.948, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.950, test=0.561) total time= 0.6s [CV 7/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.947, test=0.523) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.942, test=0.566) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010225563909774435;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.535) total time= 0.6s [CV 4/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.948, test=0.560) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.561) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.523) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.942, test=0.566) total time= 0.6s [CV 9/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.538) total time= 0.6s [CV 2/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.948, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.561) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.523) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.942, test=0.566) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.538) total time= 0.6s [CV 2/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.948, test=0.560) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.950, test=0.561) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.946, test=0.523) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.942, test=0.566) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001022977022977023;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.947, test=0.538) total time= 0.6s [CV 2/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.948, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.946, test=0.523) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.942, test=0.565) total time= 0.5s [CV 9/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001023451465508783;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.954, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.946, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.948, test=0.560) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.946, test=0.523) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.942, test=0.565) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010238095238095235;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.953, test=0.541) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.948, test=0.560) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.942, test=0.565) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.944, test=0.563) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010242703533026106;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.953, test=0.542) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.948, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010247485847485848;, score=(train=0.947, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.948, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.941, test=0.564) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.944, test=0.563) total time= 0.5s [CV 10/10] END ccp_alpha=0.0001025044091710759;, score=(train=0.947, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.953, test=0.543) total time= 0.6s [CV 3/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.950, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.948, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.946, test=0.522) total time= 0.6s [CV 8/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.941, test=0.564) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.944, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010259740259740261;, score=(train=0.947, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.947, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.949, test=0.535) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.948, test=0.560) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.944, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010265328246209264;, score=(train=0.947, test=0.522) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.941, test=0.564) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714282;, score=(train=0.946, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time= 0.6s [CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time= 0.6s [CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time= 0.6s [CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time= 0.6s [CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.953, test=0.543) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.941, test=0.564) total time= 0.6s [CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.536) total time= 0.6s [CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.943, test=0.562) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.949, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.6s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.6s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.5s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.5s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.5s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.5s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.5s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.5s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.6s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.8s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.6s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.5s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.5s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.948, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.949, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010285714285714288;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.949, test=0.536) total time= 0.5s [CV 4/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.948, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.949, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.943, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010285714285714293;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.945, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.948, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.949, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010288329519450795;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.945, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.947, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.949, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.946, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.941, test=0.563) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010298850574712658;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.949, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.945, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.947, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.949, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.943, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001030612244897959;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.949, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.945, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.947, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.949, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010308123249299722;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.953, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.945, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.947, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.949, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.946, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.943, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010309289707785953;, score=(train=0.946, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.953, test=0.543) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.945, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.947, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.949, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.941, test=0.564) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.943, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010309523809523812;, score=(train=0.946, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.946, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.952, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.945, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.947, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.948, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010322094055013314;, score=(train=0.946, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.952, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.945, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.947, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.948, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.946, test=0.522) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.941, test=0.564) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.943, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010323809523809529;, score=(train=0.946, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.946, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.952, test=0.544) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.945, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.947, test=0.559) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.948, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.946, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.941, test=0.564) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010333333333333334;, score=(train=0.946, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.945, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.952, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.949, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.945, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.947, test=0.559) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.948, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.945, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.940, test=0.565) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010337026239067059;, score=(train=0.946, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.944, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.952, test=0.543) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.948, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.945, test=0.526) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.946, test=0.560) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.948, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.945, test=0.521) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.939, test=0.565) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.943, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010358543417366968;, score=(train=0.945, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.943, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.950, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.948, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.943, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.945, test=0.560) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.944, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.939, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.942, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010372294372294374;, score=(train=0.945, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.943, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.950, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.948, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.943, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.945, test=0.560) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.944, test=0.522) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.939, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.942, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010373626373626378;, score=(train=0.945, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.943, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.950, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.948, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.942, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.945, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.943, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.938, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.942, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010389115646258502;, score=(train=0.945, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.949, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.942, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.947, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.943, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.937, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.941, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001038961038961039;, score=(train=0.944, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.942, test=0.542) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.949, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.947, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.942, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.944, test=0.561) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.946, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.942, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.936, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.941, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010418470418470426;, score=(train=0.944, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.942, test=0.542) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.949, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.946, test=0.534) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.941, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.944, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.946, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.942, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.936, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.941, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010426065162907272;, score=(train=0.944, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.948, test=0.539) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.946, test=0.534) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.943, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.942, test=0.523) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.936, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.941, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010448979591836735;, score=(train=0.944, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.941, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.948, test=0.539) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.945, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.941, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.942, test=0.564) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.944, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.942, test=0.523) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.936, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.940, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001046176046176046;, score=(train=0.943, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.948, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.945, test=0.532) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.942, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.936, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.940, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.948, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.945, test=0.532) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.942, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.941, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.936, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.940, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010476190476190477;, score=(train=0.943, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.941, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.948, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.945, test=0.532) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.941, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.942, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.943, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.941, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.936, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.940, test=0.561) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010476190476190481;, score=(train=0.943, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.941, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.948, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.944, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.941, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.942, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.943, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.941, test=0.524) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.936, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.939, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010490620490620489;, score=(train=0.943, test=0.522) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.941, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.947, test=0.541) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.944, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.941, test=0.527) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.942, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.943, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.941, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.935, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.939, test=0.561) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010504046677959723;, score=(train=0.943, test=0.522) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.941, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.947, test=0.541) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.944, test=0.532) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.940, test=0.527) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.942, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.942, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.941, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.935, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.939, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010507389162561578;, score=(train=0.942, test=0.522) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.940, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.946, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.944, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.940, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.942, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.942, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.940, test=0.524) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.935, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.939, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010524565381708241;, score=(train=0.942, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.940, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.946, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.944, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.940, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.942, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.942, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.940, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.935, test=0.566) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.939, test=0.560) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010526315789473682;, score=(train=0.942, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.940, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.946, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.944, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.940, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.942, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.942, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.940, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.935, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.939, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010530612244897958;, score=(train=0.942, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.940, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.946, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.944, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.940, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.942, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.942, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.940, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.935, test=0.566) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.939, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010530612244897961;, score=(train=0.942, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.940, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.946, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.944, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.939, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.941, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.942, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.940, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.935, test=0.565) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.939, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010535064935064925;, score=(train=0.942, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.940, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.946, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.944, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.939, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.941, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.942, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.939, test=0.525) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.935, test=0.565) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.939, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010540479928235033;, score=(train=0.942, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.940, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.946, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.944, test=0.531) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.939, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.941, test=0.565) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.942, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.939, test=0.525) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.935, test=0.565) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.939, test=0.560) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010544217687074828;, score=(train=0.942, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.946, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.943, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.528) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.941, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.935, test=0.565) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.946, test=0.540) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.943, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.941, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.524) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.935, test=0.565) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.939, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010549450549450546;, score=(train=0.942, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.939, test=0.541) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.945, test=0.540) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.942, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.939, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.941, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.942, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.939, test=0.525) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.935, test=0.565) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.938, test=0.558) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010571884256094779;, score=(train=0.941, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.935, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.941, test=0.541) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.939, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.934, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.936, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.938, test=0.565) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.935, test=0.528) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.931, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.933, test=0.560) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010590659340659335;, score=(train=0.936, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.935, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.941, test=0.542) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.938, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.934, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.936, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.937, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.935, test=0.529) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.931, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.933, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010606516290726832;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.941, test=0.542) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.938, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.934, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.935, test=0.528) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.931, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.932, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010612244897959187;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.934, test=0.539) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.941, test=0.542) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.938, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.934, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.935, test=0.562) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.936, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.934, test=0.528) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.931, test=0.567) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.932, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010621118012422355;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.939, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.937, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.935, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.529) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.939, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.937, test=0.533) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.935, test=0.562) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.934, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.933, test=0.529) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.567) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.930, test=0.562) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001064773735581189;, score=(train=0.936, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.537) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.938, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.535) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.932, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.561) total time= 0.5s [CV 6/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.529) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.568) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.537) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.938, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.535) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.932, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.934, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.933, test=0.529) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.568) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.930, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010666666666666663;, score=(train=0.936, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.933, test=0.537) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.938, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.936, test=0.535) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.932, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.934, test=0.561) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.934, test=0.564) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.933, test=0.529) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.930, test=0.568) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.930, test=0.561) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010666666666666664;, score=(train=0.936, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.933, test=0.537) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.938, test=0.544) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.936, test=0.535) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.932, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.934, test=0.562) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.934, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.933, test=0.529) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.930, test=0.568) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.930, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.935, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.537) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.938, test=0.544) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.936, test=0.535) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.932, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.530) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.568) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.561) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.935, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.537) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.938, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.936, test=0.535) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.932, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.934, test=0.564) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.933, test=0.530) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.568) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.930, test=0.561) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.935, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.933, test=0.537) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.938, test=0.544) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.935, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.932, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.934, test=0.562) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.934, test=0.564) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.933, test=0.530) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.930, test=0.568) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010677655677655675;, score=(train=0.935, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.537) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.938, test=0.544) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.935, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.931, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.933, test=0.530) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.930, test=0.569) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010686456400742117;, score=(train=0.935, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.932, test=0.537) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.938, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.935, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.931, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.933, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.933, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.933, test=0.530) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.930, test=0.569) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010691244239631333;, score=(train=0.934, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.932, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.938, test=0.544) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.935, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.931, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.932, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.933, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.933, test=0.530) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.929, test=0.570) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010701298701298702;, score=(train=0.934, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.932, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.938, test=0.545) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.935, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.931, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.932, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.933, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.932, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.929, test=0.570) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010708028440060383;, score=(train=0.934, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.938, test=0.545) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.931, test=0.533) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.933, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.938, test=0.545) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.931, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.933, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.938, test=0.545) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.931, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.933, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.932, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.929, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010714285714285714;, score=(train=0.934, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.932, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.938, test=0.545) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.933, test=0.535) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.931, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.931, test=0.563) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.932, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.932, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.928, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.928, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010730158730158743;, score=(train=0.933, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.932, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.938, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.933, test=0.535) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.930, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.931, test=0.563) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.932, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.932, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.928, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.928, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001073949579831933;, score=(train=0.933, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.931, test=0.538) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.938, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.932, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.929, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.930, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.932, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.931, test=0.531) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.927, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.926, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001075488721804511;, score=(train=0.932, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.931, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.938, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.932, test=0.537) total time= 0.4s [CV 4/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.929, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.930, test=0.565) total time= 0.4s [CV 6/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.932, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.931, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.927, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.926, test=0.564) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010756302521008405;, score=(train=0.932, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.938, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.537) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.929, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.930, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.531) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.927, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.926, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.938, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.537) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.929, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.930, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.564) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.931, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.927, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.926, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010756302521008406;, score=(train=0.932, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.938, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.928, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.930, test=0.564) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.926, test=0.573) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.926, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010767507002801114;, score=(train=0.931, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.931, test=0.538) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.938, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.927, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.930, test=0.565) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.931, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.930, test=0.531) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.926, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.925, test=0.565) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010793153807955848;, score=(train=0.931, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.929, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.937, test=0.548) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.927, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.929, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.931, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.926, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.925, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010799745458130552;, score=(train=0.931, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.929, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.937, test=0.548) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.929, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.931, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.926, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.924, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010809042809042805;, score=(train=0.930, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.929, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.937, test=0.548) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.926, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.929, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.931, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.926, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.924, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010812121212121213;, score=(train=0.930, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.929, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.937, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.929, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.931, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.926, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.924, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010817417876241409;, score=(train=0.930, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.929, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.937, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.929, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.931, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.926, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.924, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010820170820170837;, score=(train=0.930, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.929, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.937, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.929, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.931, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.926, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.924, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010822510822510817;, score=(train=0.930, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.929, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.937, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.930, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.929, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.931, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.926, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.924, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010822510822510825;, score=(train=0.930, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.929, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.937, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.930, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.928, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.931, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.926, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.924, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010827067669172938;, score=(train=0.930, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.928, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.937, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.926, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.928, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.925, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.923, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010845938375350146;, score=(train=0.930, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.928, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.937, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.928, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.925, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.923, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010848685420113992;, score=(train=0.930, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.928, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.937, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.930, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.926, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.928, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.930, test=0.565) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.929, test=0.532) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.925, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.923, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010851428113332873;, score=(train=0.930, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.928, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.937, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.930, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.926, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.928, test=0.566) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.930, test=0.565) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.929, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.925, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.923, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010852625635234328;, score=(train=0.930, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.927, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.936, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.930, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.925, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.928, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.930, test=0.565) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.929, test=0.532) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.925, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.922, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001086996336996337;, score=(train=0.929, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.927, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.936, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.929, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.925, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.928, test=0.567) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.930, test=0.565) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.929, test=0.534) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.925, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.922, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010884353741496596;, score=(train=0.928, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.927, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.936, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.929, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.925, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.927, test=0.569) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.929, test=0.564) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.929, test=0.534) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.925, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.922, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010884353741496598;, score=(train=0.928, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.000108843537414966;, score=(train=0.933, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.574) total time= 0.3s [CV 6/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.535) total time= 0.3s [CV 8/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.000108843537414966;, score=(train=0.919, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.000108843537414966;, score=(train=0.933, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.000108843537414966;, score=(train=0.924, test=0.574) total time= 0.3s [CV 6/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.000108843537414966;, score=(train=0.925, test=0.535) total time= 0.3s [CV 8/10] END ccp_alpha=0.000108843537414966;, score=(train=0.922, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.000108843537414966;, score=(train=0.919, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.000108843537414966;, score=(train=0.926, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.933, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.925, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.922, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.924, test=0.574) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.926, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.925, test=0.535) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.922, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.919, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010884353741496602;, score=(train=0.926, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.933, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.925, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.922, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.924, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.926, test=0.562) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.925, test=0.536) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.922, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.919, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010888888888888889;, score=(train=0.925, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.933, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.925, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.922, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.924, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.926, test=0.562) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.925, test=0.536) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.922, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.919, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010889627168696932;, score=(train=0.925, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.932, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.925, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.921, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.924, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.926, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.924, test=0.536) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.922, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.919, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.925, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.932, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.925, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.921, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.924, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.926, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.924, test=0.536) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.922, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.919, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010909090909090915;, score=(train=0.925, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.932, test=0.546) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.925, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.921, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.924, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.926, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.923, test=0.537) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.922, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.918, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001092783505154639;, score=(train=0.925, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.932, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.925, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.921, test=0.529) total time= 0.4s [CV 5/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.924, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.926, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.922, test=0.539) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.922, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.918, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010930552186918908;, score=(train=0.925, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.924, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.932, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.925, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.921, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.924, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.926, test=0.563) total time= 0.4s [CV 7/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.922, test=0.539) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.922, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.918, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010931677018633544;, score=(train=0.925, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.924, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.931, test=0.546) total time= 0.4s [CV 3/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.924, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.920, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.923, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.925, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.921, test=0.540) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.921, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.918, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010959383753501403;, score=(train=0.924, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.000109624060150376;, score=(train=0.924, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.000109624060150376;, score=(train=0.931, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.000109624060150376;, score=(train=0.924, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.000109624060150376;, score=(train=0.920, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.000109624060150376;, score=(train=0.923, test=0.573) total time= 0.3s [CV 6/10] END ccp_alpha=0.000109624060150376;, score=(train=0.925, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.000109624060150376;, score=(train=0.921, test=0.540) total time= 0.3s [CV 8/10] END ccp_alpha=0.000109624060150376;, score=(train=0.921, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.000109624060150376;, score=(train=0.918, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.000109624060150376;, score=(train=0.924, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.924, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.931, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.924, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.920, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.923, test=0.574) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.925, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.920, test=0.540) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.921, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.918, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010971428571428547;, score=(train=0.924, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.924, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.931, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.924, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.920, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.923, test=0.574) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.925, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.920, test=0.540) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.921, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.918, test=0.564) total time= 0.4s [CV 10/10] END ccp_alpha=0.00010971428571428569;, score=(train=0.924, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.924, test=0.539) total time= 0.4s [CV 2/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.931, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.924, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.920, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.923, test=0.574) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.925, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.920, test=0.540) total time= 0.3s [CV 8/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.921, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.918, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010975056689342398;, score=(train=0.924, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.923, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.931, test=0.546) total time= 0.3s [CV 3/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.923, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.920, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.923, test=0.575) total time= 0.3s [CV 6/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.925, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.920, test=0.540) total time= 0.4s [CV 8/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.921, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.918, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00010984126984126983;, score=(train=0.924, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.921, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.930, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.921, test=0.538) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.919, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.922, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.924, test=0.563) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.918, test=0.544) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.919, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.916, test=0.563) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001101892393320965;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.930, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.922, test=0.576) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.918, test=0.544) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.916, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.930, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.921, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.922, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.918, test=0.544) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.919, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.916, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011020408163265303;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.930, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.538) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.922, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.918, test=0.544) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.916, test=0.563) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.930, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.921, test=0.538) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.922, test=0.576) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.918, test=0.544) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.919, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.916, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011020408163265305;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.921, test=0.540) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.930, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.918, test=0.529) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.922, test=0.576) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.918, test=0.544) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.919, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.916, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011024975024975029;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.921, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.930, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.920, test=0.539) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.918, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.922, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.918, test=0.544) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.919, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.916, test=0.563) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011030873888016743;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.920, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.929, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.918, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.922, test=0.576) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.918, test=0.543) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.919, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.916, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001104363835398318;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.920, test=0.540) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.929, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.918, test=0.528) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.922, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.924, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.918, test=0.543) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.919, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.916, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011047619047619042;, score=(train=0.923, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.929, test=0.548) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.916, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.923, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.918, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.918, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001107067578612294;, score=(train=0.922, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.929, test=0.548) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.916, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.923, test=0.564) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.918, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.918, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001107268170426066;, score=(train=0.922, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.929, test=0.548) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.916, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.923, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.918, test=0.542) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.918, test=0.577) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011072736787022503;, score=(train=0.922, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.929, test=0.548) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.916, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.923, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.918, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.918, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001107273678702253;, score=(train=0.922, test=0.530) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.927, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.530) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.921, test=0.576) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.923, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.917, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.918, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011082251082251083;, score=(train=0.922, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.919, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.927, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.915, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.921, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.923, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.917, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.918, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011086474501108669;, score=(train=0.921, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.919, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.927, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.920, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.915, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.921, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.922, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.917, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.918, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011092436974789919;, score=(train=0.921, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.919, test=0.539) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.927, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.919, test=0.539) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.915, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.920, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.922, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.917, test=0.542) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.917, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.916, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011100386100386102;, score=(train=0.921, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.919, test=0.539) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.926, test=0.547) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.919, test=0.539) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.915, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.920, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.922, test=0.564) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.917, test=0.543) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.917, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.915, test=0.562) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.921, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.916, test=0.542) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.916, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.918, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.918, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.911, test=0.547) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.916, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.914, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011131652661064425;, score=(train=0.917, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.916, test=0.542) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.915, test=0.534) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.917, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.918, test=0.567) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.911, test=0.547) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.916, test=0.579) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.914, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011145578231292516;, score=(train=0.917, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.916, test=0.542) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.915, test=0.534) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.917, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.918, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.911, test=0.547) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.915, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.912, test=0.563) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011163605442176877;, score=(train=0.916, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.542) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.534) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.917, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.918, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.547) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.912, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.542) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.534) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.917, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.918, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.911, test=0.547) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.915, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.912, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011164274322169053;, score=(train=0.916, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.916, test=0.542) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.914, test=0.534) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.917, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.917, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.910, test=0.547) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.915, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.912, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011174603174603173;, score=(train=0.916, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.916, test=0.542) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.914, test=0.534) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.917, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.917, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.910, test=0.547) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.915, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.912, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011174603174603176;, score=(train=0.916, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.915, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.914, test=0.534) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.911, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.917, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.917, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.910, test=0.547) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.915, test=0.578) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.912, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011180124223602485;, score=(train=0.916, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.915, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.924, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.914, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.910, test=0.532) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.917, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.915, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.912, test=0.563) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011191963996887969;, score=(train=0.914, test=0.531) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.915, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.923, test=0.550) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.913, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.916, test=0.568) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011206065759637187;, score=(train=0.914, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.915, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.923, test=0.549) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.913, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.916, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011217948717948719;, score=(train=0.914, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.915, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.923, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.913, test=0.533) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.916, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011220779220779233;, score=(train=0.914, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.915, test=0.543) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.923, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.913, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.916, test=0.569) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.909, test=0.548) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011227087326158537;, score=(train=0.914, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.915, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.923, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.913, test=0.531) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.916, test=0.569) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.912, test=0.564) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011228070175438597;, score=(train=0.914, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.915, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.923, test=0.549) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.913, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.916, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.916, test=0.569) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011228070175438602;, score=(train=0.914, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.914, test=0.544) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.923, test=0.550) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.913, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.916, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.909, test=0.548) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011245776779611358;, score=(train=0.913, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.914, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.923, test=0.550) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.913, test=0.531) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.916, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011250000000000002;, score=(train=0.913, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.914, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.923, test=0.550) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.913, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.916, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.916, test=0.568) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011252747252747252;, score=(train=0.913, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.914, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.923, test=0.550) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.913, test=0.531) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.916, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.916, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.909, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.914, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.912, test=0.564) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011252747252747254;, score=(train=0.913, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011283214140357;, score=(train=0.914, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011283214140357;, score=(train=0.922, test=0.550) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011283214140357;, score=(train=0.912, test=0.532) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011283214140357;, score=(train=0.910, test=0.532) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011283214140357;, score=(train=0.914, test=0.580) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011283214140357;, score=(train=0.915, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011283214140357;, score=(train=0.908, test=0.547) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011283214140357;, score=(train=0.913, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011283214140357;, score=(train=0.911, test=0.565) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011283214140357;, score=(train=0.912, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.912, test=0.542) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.920, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.910, test=0.535) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.907, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.912, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.913, test=0.569) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.905, test=0.546) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.910, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.909, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011292517006802723;, score=(train=0.909, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.912, test=0.542) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.919, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.909, test=0.536) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.907, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.912, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.912, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.905, test=0.546) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.910, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.909, test=0.566) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011306633931768202;, score=(train=0.909, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.912, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.919, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.909, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.907, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.912, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.912, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.905, test=0.546) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.910, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.909, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001130712284913965;, score=(train=0.909, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.912, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.919, test=0.554) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.909, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.907, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.911, test=0.582) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.912, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.905, test=0.546) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.910, test=0.577) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.909, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011310462536268989;, score=(train=0.909, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.912, test=0.543) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.919, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.909, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.907, test=0.531) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.911, test=0.582) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.912, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.905, test=0.546) total time= 0.2s [CV 8/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.910, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.908, test=0.565) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011319136350192253;, score=(train=0.909, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.911, test=0.545) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.919, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.909, test=0.536) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.906, test=0.531) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.910, test=0.584) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.909, test=0.569) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.905, test=0.546) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.910, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.907, test=0.565) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011337868480725617;, score=(train=0.908, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.911, test=0.545) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.917, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.909, test=0.537) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.906, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.909, test=0.583) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.907, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.903, test=0.544) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.909, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.906, test=0.565) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001138706570556688;, score=(train=0.907, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.911, test=0.545) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.917, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.909, test=0.537) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.906, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.909, test=0.583) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.907, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.903, test=0.544) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.909, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.906, test=0.565) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011387755102040813;, score=(train=0.907, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.910, test=0.545) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.917, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.909, test=0.537) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.906, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.909, test=0.583) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.907, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.903, test=0.544) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.909, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.906, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011394110275689221;, score=(train=0.906, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.910, test=0.545) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.917, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.908, test=0.538) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.905, test=0.534) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.908, test=0.583) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.907, test=0.566) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.903, test=0.544) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.908, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.906, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011418313039445993;, score=(train=0.906, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.910, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.916, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.583) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.906, test=0.565) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.903, test=0.546) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.907, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.904, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.910, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.916, test=0.554) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.539) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.533) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.908, test=0.583) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.906, test=0.565) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.903, test=0.546) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.907, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.904, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011428571428571428;, score=(train=0.905, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.908, test=0.546) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.915, test=0.554) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.541) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.903, test=0.534) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.566) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.901, test=0.547) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.905, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.903, test=0.565) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011461697722567289;, score=(train=0.902, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.907, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.914, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.905, test=0.542) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.901, test=0.537) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.904, test=0.580) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.904, test=0.566) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.900, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.904, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.902, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011494252873563225;, score=(train=0.902, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.907, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.914, test=0.555) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.905, test=0.542) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.901, test=0.537) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.904, test=0.580) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.904, test=0.566) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.900, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.903, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.902, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001149659863945579;, score=(train=0.902, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.907, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.913, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.904, test=0.542) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.901, test=0.537) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.904, test=0.580) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.903, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.900, test=0.548) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.903, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.902, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011520244461420934;, score=(train=0.902, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.907, test=0.546) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.913, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.904, test=0.543) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.901, test=0.537) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.903, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.903, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.900, test=0.548) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.903, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.902, test=0.566) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011529634053982288;, score=(train=0.901, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.906, test=0.546) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.912, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.903, test=0.544) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.899, test=0.537) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.902, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.902, test=0.566) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.899, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.902, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.901, test=0.567) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001154887218045113;, score=(train=0.900, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.904, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.912, test=0.556) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.903, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.898, test=0.537) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.900, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.901, test=0.566) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.898, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.902, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.899, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011610256410256408;, score=(train=0.899, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.904, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.912, test=0.556) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.903, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.898, test=0.537) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.900, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.901, test=0.566) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.898, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.902, test=0.577) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.899, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011610675039246468;, score=(train=0.899, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.902, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.910, test=0.556) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.902, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.895, test=0.539) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.899, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.899, test=0.567) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.897, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.900, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.896, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001166842233206386;, score=(train=0.898, test=0.528) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.902, test=0.544) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.910, test=0.556) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.902, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.895, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.899, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.899, test=0.567) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.897, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.900, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.896, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011676701331873743;, score=(train=0.897, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.901, test=0.545) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.910, test=0.556) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.902, test=0.544) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.895, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.899, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.898, test=0.567) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.897, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.899, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.896, test=0.571) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001169380560684907;, score=(train=0.897, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.900, test=0.546) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.909, test=0.555) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.901, test=0.543) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.895, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.899, test=0.578) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.898, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.897, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.899, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.895, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011716731249461808;, score=(train=0.897, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.900, test=0.546) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.909, test=0.556) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.901, test=0.543) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.898, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.550) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.900, test=0.546) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.909, test=0.556) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.901, test=0.543) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.539) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.898, test=0.568) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.899, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.895, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011721611721611714;, score=(train=0.897, test=0.529) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.908, test=0.557) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.901, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.898, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.571) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.896, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.908, test=0.557) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.901, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.898, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.896, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.908, test=0.557) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.901, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.898, test=0.578) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.897, test=0.549) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.899, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.894, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011721611721611721;, score=(train=0.896, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.899, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.908, test=0.557) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.901, test=0.544) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.894, test=0.539) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.897, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.897, test=0.549) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.898, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.894, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011722972972972968;, score=(train=0.896, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.899, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.908, test=0.557) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.901, test=0.544) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.894, test=0.541) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.897, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.897, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.898, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.893, test=0.572) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011735109335109332;, score=(train=0.896, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.899, test=0.547) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.907, test=0.556) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.901, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.893, test=0.540) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.897, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.893, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011755102040816325;, score=(train=0.895, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.898, test=0.548) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.906, test=0.560) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.901, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.892, test=0.541) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.897, test=0.571) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.890, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011775304061018336;, score=(train=0.895, test=0.529) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.898, test=0.548) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.906, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.901, test=0.545) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.892, test=0.541) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.897, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.890, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011778790315375675;, score=(train=0.894, test=0.526) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.898, test=0.548) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.906, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.901, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.892, test=0.541) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.897, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.897, test=0.571) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.897, test=0.549) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.896, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.890, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011789473684210546;, score=(train=0.893, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.898, test=0.548) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.904, test=0.559) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.900, test=0.544) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.892, test=0.541) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.896, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.896, test=0.573) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.896, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.895, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.890, test=0.572) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011813151927437645;, score=(train=0.893, test=0.525) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.898, test=0.548) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.904, test=0.559) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.900, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.892, test=0.541) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.896, test=0.577) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.896, test=0.572) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.896, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.895, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.890, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011826415094339623;, score=(train=0.893, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.898, test=0.548) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.903, test=0.559) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.900, test=0.544) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.892, test=0.541) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.896, test=0.577) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.896, test=0.572) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.896, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.895, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.888, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011837449279904471;, score=(train=0.893, test=0.526) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.896, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.903, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.899, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.890, test=0.542) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.894, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.893, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.896, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.894, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.886, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011859555083815222;, score=(train=0.891, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.896, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.903, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.899, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.890, test=0.542) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.892, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.893, test=0.574) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.896, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.894, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.886, test=0.572) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001186904761904762;, score=(train=0.890, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.896, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.903, test=0.560) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.899, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.890, test=0.542) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.892, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.893, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.896, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.894, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.886, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011869971285488915;, score=(train=0.890, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.896, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.903, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.899, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.890, test=0.542) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.892, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.893, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.896, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.894, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.886, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011870748299319726;, score=(train=0.890, test=0.524) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.896, test=0.550) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.903, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.899, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.888, test=0.545) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.892, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.892, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.896, test=0.549) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.893, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.885, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011896222318714727;, score=(train=0.890, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.550) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.903, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.898, test=0.545) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.887, test=0.545) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.891, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.892, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.893, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.885, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.890, test=0.523) total time= 0.4s [CV 1/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.550) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.903, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.898, test=0.545) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.887, test=0.545) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.891, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.892, test=0.574) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.896, test=0.549) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.893, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.885, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.890, test=0.523) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.895, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.900, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.898, test=0.547) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.886, test=0.547) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.892, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.884, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.889, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.895, test=0.549) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.900, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.898, test=0.547) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.886, test=0.547) total time= 0.4s [CV 5/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.890, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.892, test=0.571) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.884, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011948051948051947;, score=(train=0.889, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.894, test=0.549) total time= 0.4s [CV 2/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.899, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.897, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.890, test=0.580) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.894, test=0.552) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.891, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.882, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011964307801042495;, score=(train=0.888, test=0.525) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.894, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.899, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.897, test=0.553) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.890, test=0.580) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.894, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.891, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.882, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011965811965811959;, score=(train=0.888, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.894, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.899, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.897, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.890, test=0.580) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.889, test=0.575) total time= 0.4s [CV 7/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.894, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.891, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.882, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011966760037348276;, score=(train=0.888, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.894, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.899, test=0.560) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.897, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.889, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.894, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.891, test=0.572) total time= 0.4s [CV 9/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.882, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011980145686134803;, score=(train=0.888, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.894, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.898, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.897, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.889, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.894, test=0.551) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.891, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.881, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011992911277432239;, score=(train=0.888, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.893, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.898, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.896, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.889, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.894, test=0.551) total time= 0.4s [CV 8/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.890, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011998631380074655;, score=(train=0.888, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.893, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.898, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.896, test=0.553) total time= 0.4s [CV 4/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.889, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.890, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.881, test=0.570) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011999999999999994;, score=(train=0.888, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.893, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.898, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.896, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.889, test=0.578) total time= 0.4s [CV 6/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.890, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.888, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.893, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.898, test=0.562) total time= 0.4s [CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.896, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.889, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.890, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.881, test=0.570) total time= 0.4s [CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.888, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.893, test=0.550) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.898, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.896, test=0.553) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.885, test=0.546) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.888, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.890, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012013237727523439;, score=(train=0.888, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.893, test=0.550) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.898, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.896, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.888, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.889, test=0.575) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.890, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012015810276679847;, score=(train=0.888, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.893, test=0.550) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.898, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.896, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.888, test=0.578) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.890, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.881, test=0.570) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012019849035504592;, score=(train=0.888, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.893, test=0.549) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.898, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.896, test=0.553) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.888, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.889, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.894, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.890, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012023589038035246;, score=(train=0.888, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.893, test=0.550) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.897, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.895, test=0.554) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.885, test=0.546) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.887, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.888, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.893, test=0.551) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.887, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001203194321206745;, score=(train=0.887, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.893, test=0.550) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.897, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.895, test=0.554) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.885, test=0.546) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.887, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.888, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.893, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.887, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001203290246768507;, score=(train=0.887, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.892, test=0.551) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.897, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.895, test=0.554) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.885, test=0.547) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.887, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.888, test=0.575) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.893, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.886, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012056514913657773;, score=(train=0.887, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.891, test=0.551) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.897, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.895, test=0.554) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.884, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.886, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.887, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.892, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.885, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.881, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012071428571428568;, score=(train=0.887, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.890, test=0.552) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.897, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.893, test=0.555) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.884, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.886, test=0.579) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.887, test=0.574) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.892, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.885, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.880, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012093533065400073;, score=(train=0.887, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.889, test=0.553) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.897, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.893, test=0.555) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.884, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.886, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.887, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.892, test=0.552) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.885, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.880, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012097598158857216;, score=(train=0.887, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.889, test=0.554) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.897, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.893, test=0.555) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.884, test=0.549) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.886, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.887, test=0.575) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.892, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.885, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.880, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012098301604134064;, score=(train=0.887, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.888, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.896, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.883, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.886, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.886, test=0.578) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.891, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.884, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.880, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012121212121212128;, score=(train=0.886, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.888, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.896, test=0.562) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.883, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.885, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.891, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.884, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.880, test=0.569) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012130857648099023;, score=(train=0.886, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.888, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.896, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.883, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.885, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.891, test=0.552) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.883, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.879, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012140452140452142;, score=(train=0.885, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.888, test=0.555) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.896, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.883, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.884, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.891, test=0.552) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.883, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.879, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012146939202063245;, score=(train=0.885, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.888, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.896, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.892, test=0.556) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.883, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.884, test=0.579) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.891, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.883, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.879, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012147233893557418;, score=(train=0.885, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.888, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.895, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.884, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.886, test=0.578) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.890, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.883, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.878, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012170473609497997;, score=(train=0.885, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.888, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.895, test=0.562) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.884, test=0.581) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.890, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.883, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.878, test=0.569) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012172684001579689;, score=(train=0.885, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.887, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.895, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.883, test=0.581) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.890, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.882, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.878, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001218297830862781;, score=(train=0.885, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.887, test=0.555) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.895, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.883, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.890, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.882, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.878, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012190476190476187;, score=(train=0.885, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.887, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.895, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.882, test=0.550) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.883, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.886, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.890, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.882, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.878, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001219047619047619;, score=(train=0.885, test=0.527) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.886, test=0.555) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.895, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.892, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.883, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.885, test=0.578) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.890, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.882, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.877, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012195881273638954;, score=(train=0.884, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.886, test=0.554) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.895, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.891, test=0.556) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.882, test=0.550) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.883, test=0.581) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.885, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.890, test=0.552) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.882, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.877, test=0.570) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012201518288474812;, score=(train=0.884, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.886, test=0.554) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.895, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.891, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.883, test=0.581) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.885, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.890, test=0.552) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.882, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.877, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012201680672268905;, score=(train=0.884, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.886, test=0.554) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.894, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.891, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.883, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.885, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.890, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.882, test=0.573) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.877, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012218181818181816;, score=(train=0.884, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.886, test=0.554) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.894, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.891, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.882, test=0.550) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.883, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.885, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.890, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.882, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.877, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012224338624338609;, score=(train=0.884, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.885, test=0.554) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.894, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.891, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.882, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.884, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.890, test=0.553) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.881, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.877, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001224237843285461;, score=(train=0.883, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.885, test=0.554) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.894, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.891, test=0.556) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.882, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.882, test=0.581) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.884, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.890, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.881, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.877, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012242400873979837;, score=(train=0.883, test=0.527) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.885, test=0.556) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.891, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.881, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.882, test=0.582) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.884, test=0.579) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.881, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.876, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012244897959183676;, score=(train=0.883, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.885, test=0.556) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.893, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.890, test=0.556) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.881, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.882, test=0.582) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.884, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.881, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.876, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012251815980629565;, score=(train=0.883, test=0.528) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.884, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.879, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.880, test=0.584) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.884, test=0.579) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.880, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.875, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012276792198360857;, score=(train=0.881, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.884, test=0.557) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.879, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.880, test=0.584) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.883, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.880, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.875, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012278968746290645;, score=(train=0.881, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.884, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.879, test=0.550) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.879, test=0.584) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.883, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.880, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.875, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001231020408163266;, score=(train=0.879, test=0.531) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.883, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.882, test=0.580) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.874, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012326530612244904;, score=(train=0.878, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.883, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.878, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.882, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.874, test=0.569) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012336555674901536;, score=(train=0.878, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.883, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.878, test=0.586) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.882, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.879, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.874, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012338538654328127;, score=(train=0.878, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.883, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.893, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.882, test=0.580) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.889, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.879, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.873, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012344882170847314;, score=(train=0.878, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.883, test=0.557) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.882, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.553) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.873, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.883, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.882, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.889, test=0.553) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.873, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012346938775510197;, score=(train=0.878, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.883, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.893, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.889, test=0.558) total time= 0.5s [CV 4/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.882, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.873, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001235113035113036;, score=(train=0.878, test=0.530) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.883, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.893, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.888, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.881, test=0.579) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.873, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012363909774436093;, score=(train=0.878, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.882, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.893, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.888, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.881, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.879, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.873, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012379893349281101;, score=(train=0.878, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.882, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.888, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.878, test=0.587) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.881, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.873, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012380952380952376;, score=(train=0.878, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.882, test=0.558) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.888, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.881, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.872, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012380952380952384;, score=(train=0.878, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.882, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.888, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.877, test=0.549) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.881, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.872, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012392290249433105;, score=(train=0.878, test=0.531) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.882, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.888, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.878, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.881, test=0.579) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.879, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.872, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012400960384153668;, score=(train=0.878, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.881, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.887, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.877, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.880, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.872, test=0.568) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012439560439560438;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.881, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.887, test=0.559) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.876, test=0.585) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.880, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.872, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012440763745111575;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.881, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.887, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.876, test=0.585) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.880, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.876, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.872, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001245036201027363;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.881, test=0.557) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.887, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.876, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.880, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.872, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012462693120942519;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.881, test=0.557) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.887, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.875, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.880, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.888, test=0.553) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.871, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.881, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.887, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.875, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.880, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.871, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012467532467532467;, score=(train=0.876, test=0.533) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.881, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.887, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.877, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.875, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.880, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.888, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.871, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012467683369644182;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.880, test=0.556) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.887, test=0.557) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.875, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.879, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.887, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.871, test=0.569) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012480111031194625;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.880, test=0.556) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.893, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.887, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.877, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.875, test=0.586) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.879, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.887, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.876, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.871, test=0.570) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012483516483516487;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.879, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.892, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.887, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.876, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.875, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.878, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.887, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.875, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.871, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012493312243312243;, score=(train=0.876, test=0.533) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.878, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.892, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.886, test=0.557) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.878, test=0.578) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.887, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.870, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001250216450216451;, score=(train=0.875, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.878, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.892, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.886, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.878, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.887, test=0.553) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.870, test=0.569) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012507563025210082;, score=(train=0.875, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.878, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.892, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.886, test=0.558) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.878, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.887, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.870, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012514872615712957;, score=(train=0.875, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.878, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.892, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.886, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.878, test=0.579) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.887, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.870, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012519714353403087;, score=(train=0.875, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.878, test=0.557) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.891, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.886, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.874, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.875, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.878, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.887, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.875, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.870, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012535973293426993;, score=(train=0.875, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.877, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.890, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.885, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.874, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.874, test=0.587) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.878, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.887, test=0.553) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.875, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.869, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012551495016611294;, score=(train=0.874, test=0.531) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.559) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.889, test=0.563) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.884, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.877, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.887, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.874, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.869, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012571428571428586;, score=(train=0.873, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.873, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.888, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.884, test=0.559) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.874, test=0.550) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.873, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.877, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.886, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.874, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.869, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012589497218295977;, score=(train=0.873, test=0.532) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.888, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.884, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.877, test=0.579) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.886, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.874, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.869, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012596273291925477;, score=(train=0.873, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.558) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.887, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.883, test=0.558) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.869, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.876, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.885, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.866, test=0.568) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012633766233766228;, score=(train=0.873, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.887, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.883, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.549) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.869, test=0.588) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.876, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.885, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.866, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012634299596324958;, score=(train=0.873, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.873, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.886, test=0.565) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.882, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.872, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.869, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.875, test=0.581) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.885, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.873, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.866, test=0.569) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001265735298293438;, score=(train=0.872, test=0.532) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.873, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.885, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.882, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.872, test=0.549) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.869, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.875, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.885, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.871, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.865, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012671626330162911;, score=(train=0.870, test=0.535) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.873, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.885, test=0.565) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.882, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.872, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.868, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.875, test=0.581) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.885, test=0.554) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.871, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.865, test=0.568) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012675158278972697;, score=(train=0.870, test=0.535) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.873, test=0.558) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.885, test=0.565) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.880, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.871, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.868, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.875, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.883, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.871, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.864, test=0.570) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001269841269841269;, score=(train=0.870, test=0.535) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.873, test=0.559) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.883, test=0.563) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.878, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.870, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.867, test=0.585) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.873, test=0.581) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.882, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.868, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.862, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012710323141556503;, score=(train=0.869, test=0.535) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.872, test=0.560) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.883, test=0.563) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.878, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.870, test=0.548) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.867, test=0.585) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.873, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.882, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.868, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.862, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012720538720538714;, score=(train=0.869, test=0.535) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.872, test=0.560) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.882, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.878, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.870, test=0.547) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.866, test=0.585) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.873, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.881, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.868, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.862, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012741013824884803;, score=(train=0.869, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.871, test=0.562) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.882, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.878, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.869, test=0.547) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.866, test=0.585) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.873, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.881, test=0.556) total time= 0.4s [CV 8/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.868, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.862, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012745825602968457;, score=(train=0.869, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.870, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.881, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.878, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.869, test=0.547) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.866, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.872, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.880, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.868, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.859, test=0.574) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001276595744680851;, score=(train=0.869, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.870, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.881, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.878, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.869, test=0.547) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.866, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.872, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.880, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.868, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.859, test=0.574) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012770927175264485;, score=(train=0.869, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.870, test=0.562) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.881, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.878, test=0.560) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.868, test=0.547) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.866, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.872, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.880, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.868, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.859, test=0.574) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001277728482697426;, score=(train=0.869, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.870, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.881, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.878, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.868, test=0.547) total time= 0.2s [CV 5/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.866, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.872, test=0.580) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.880, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.868, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.859, test=0.574) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012781571369806623;, score=(train=0.869, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.869, test=0.563) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.881, test=0.561) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.878, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.868, test=0.547) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.866, test=0.586) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.872, test=0.580) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.880, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.868, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.859, test=0.574) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012791254866726556;, score=(train=0.869, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.561) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.875, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.866, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.863, test=0.587) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.870, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.865, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.858, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.561) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.560) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.875, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.866, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.863, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.870, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.879, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.865, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.858, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.868, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.868, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.878, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.874, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.863, test=0.549) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.859, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.868, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.864, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.857, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012900849150849152;, score=(train=0.868, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.867, test=0.563) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.878, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.874, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.863, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.859, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.868, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.878, test=0.556) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.864, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.857, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001290666960158485;, score=(train=0.868, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.865, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.878, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.873, test=0.560) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.863, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.859, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.868, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.863, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.856, test=0.572) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012929171668667468;, score=(train=0.868, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.865, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.878, test=0.561) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.873, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.863, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.859, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.868, test=0.582) total time= 0.4s [CV 7/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.863, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.856, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012936507936507936;, score=(train=0.868, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.865, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.873, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.863, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.859, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.867, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.863, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.855, test=0.572) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012948184781386965;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.865, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.873, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.863, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.859, test=0.587) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.867, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.863, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.855, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012948549629666298;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.865, test=0.562) total time= 0.4s [CV 2/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.873, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.863, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.859, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.867, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.863, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.855, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012950191570881234;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.865, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.873, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.862, test=0.549) total time= 0.4s [CV 5/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.859, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.867, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.863, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.855, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012952380952380957;, score=(train=0.867, test=0.535) total time= 0.4s [CV 1/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.865, test=0.562) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.873, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.862, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.859, test=0.587) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.867, test=0.582) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.863, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.855, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012963646064486395;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.865, test=0.563) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.873, test=0.560) total time= 0.4s [CV 4/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.862, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.858, test=0.588) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.866, test=0.581) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.863, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.854, test=0.572) total time= 0.4s [CV 10/10] END ccp_alpha=0.00012976356976356982;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.865, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.872, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.862, test=0.549) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.858, test=0.588) total time= 0.4s [CV 6/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.865, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.878, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.862, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.854, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012984221236852809;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.864, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.878, test=0.562) total time= 0.4s [CV 3/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.872, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.862, test=0.550) total time= 0.3s [CV 5/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.858, test=0.589) total time= 0.3s [CV 6/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.865, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.877, test=0.557) total time= 0.3s [CV 8/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.861, test=0.577) total time= 0.4s [CV 9/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.854, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.00012992358175020717;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.864, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.878, test=0.562) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.872, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.861, test=0.550) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.858, test=0.589) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.865, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.876, test=0.557) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.861, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.854, test=0.572) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001301470588235294;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.864, test=0.563) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.877, test=0.563) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.871, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.861, test=0.551) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.857, test=0.589) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.865, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.876, test=0.556) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.861, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.854, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013029832400618922;, score=(train=0.867, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.864, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.876, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.871, test=0.561) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.861, test=0.551) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.857, test=0.590) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.865, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.875, test=0.557) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.860, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.854, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001304750804750805;, score=(train=0.866, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.864, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.876, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.870, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.860, test=0.551) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.857, test=0.590) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.864, test=0.585) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.875, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.860, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.854, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001306122448979592;, score=(train=0.866, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.864, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.876, test=0.564) total time= 0.4s [CV 3/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.868, test=0.563) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.860, test=0.551) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.856, test=0.590) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.864, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.875, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.859, test=0.577) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.852, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013073593073593078;, score=(train=0.866, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.863, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.876, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.868, test=0.563) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.860, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.856, test=0.591) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.864, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.874, test=0.557) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.859, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.852, test=0.574) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013093512767425818;, score=(train=0.866, test=0.536) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.863, test=0.564) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.876, test=0.564) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.867, test=0.564) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.860, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.856, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.864, test=0.584) total time= 0.4s [CV 7/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.874, test=0.557) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.859, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.852, test=0.574) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013099415204678353;, score=(train=0.866, test=0.536) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.862, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.875, test=0.566) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.866, test=0.563) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.859, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.855, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.863, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.873, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.858, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.852, test=0.574) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013141762452107244;, score=(train=0.865, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.862, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.875, test=0.566) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.866, test=0.563) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.859, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.855, test=0.590) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.863, test=0.585) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.873, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.858, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.852, test=0.574) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001314559125085441;, score=(train=0.865, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.862, test=0.564) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.875, test=0.566) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.865, test=0.563) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.859, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.855, test=0.590) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.863, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.873, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.858, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.851, test=0.573) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013154061624649858;, score=(train=0.865, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.861, test=0.565) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.874, test=0.566) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.864, test=0.564) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.854, test=0.591) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.863, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.872, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.850, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.865, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.861, test=0.565) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.874, test=0.566) total time= 0.4s [CV 3/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.864, test=0.564) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.854, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.863, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.872, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.858, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.850, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013186813186813188;, score=(train=0.865, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.861, test=0.565) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.873, test=0.568) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.864, test=0.564) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.858, test=0.554) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.854, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.863, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.871, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.858, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.850, test=0.571) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013203194321206746;, score=(train=0.865, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.860, test=0.565) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.871, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.863, test=0.564) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.857, test=0.555) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.853, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.862, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.870, test=0.553) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.858, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.848, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001323033597227146;, score=(train=0.865, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.860, test=0.565) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.871, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.863, test=0.564) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.856, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.853, test=0.592) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.862, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.867, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.858, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.848, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001323852617970265;, score=(train=0.865, test=0.537) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.859, test=0.565) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.871, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.862, test=0.563) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.856, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.852, test=0.592) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.862, test=0.586) total time= 0.4s [CV 7/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.867, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.857, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.848, test=0.573) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013251871281427932;, score=(train=0.865, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.859, test=0.566) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.871, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.862, test=0.563) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.856, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.852, test=0.592) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.860, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.866, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.857, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.847, test=0.572) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013275981278587342;, score=(train=0.864, test=0.539) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.858, test=0.566) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.868, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.859, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.855, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.851, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.860, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.855, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.846, test=0.575) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013321453321453328;, score=(train=0.863, test=0.538) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.858, test=0.566) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.868, test=0.572) total time= 0.4s [CV 3/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.859, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.855, test=0.553) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.851, test=0.592) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.859, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.855, test=0.578) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.845, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013333333333333334;, score=(train=0.862, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.858, test=0.566) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.868, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.859, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.855, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.851, test=0.592) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.859, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.855, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.845, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001333333333333335;, score=(train=0.862, test=0.537) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.858, test=0.566) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.868, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.859, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.855, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.851, test=0.592) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.859, test=0.584) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.855, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.845, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001334240362811791;, score=(train=0.861, test=0.539) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.858, test=0.566) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.868, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.859, test=0.562) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.855, test=0.553) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.851, test=0.592) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.859, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.855, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.845, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001334448931087586;, score=(train=0.861, test=0.539) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.858, test=0.566) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.868, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.858, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.855, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.851, test=0.592) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.859, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.855, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.844, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001335164835164835;, score=(train=0.861, test=0.539) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.858, test=0.566) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.867, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.858, test=0.562) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.855, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.851, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.859, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.855, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.844, test=0.575) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013356225208691064;, score=(train=0.861, test=0.539) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.857, test=0.570) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.867, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.858, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.855, test=0.553) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.851, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.859, test=0.584) total time= 0.4s [CV 7/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.854, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.844, test=0.577) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013372405372405368;, score=(train=0.860, test=0.540) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.857, test=0.570) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.867, test=0.571) total time= 0.4s [CV 3/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.857, test=0.563) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.854, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.851, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.858, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.864, test=0.550) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.852, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.843, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013394022026047483;, score=(train=0.859, test=0.541) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.856, test=0.568) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.867, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.857, test=0.563) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.853, test=0.555) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.850, test=0.593) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.858, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.863, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.852, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.843, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013411854103343463;, score=(train=0.859, test=0.541) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.856, test=0.568) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.867, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.857, test=0.563) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.853, test=0.555) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.850, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.858, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.863, test=0.551) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.852, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.843, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001341269841269841;, score=(train=0.859, test=0.541) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.856, test=0.569) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.866, test=0.570) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.857, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.852, test=0.556) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.848, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.861, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.852, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001344152899254939;, score=(train=0.859, test=0.542) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.856, test=0.569) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.866, test=0.570) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.857, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.852, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.848, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.861, test=0.551) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.851, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013443609022556382;, score=(train=0.859, test=0.542) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.856, test=0.569) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.866, test=0.570) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.857, test=0.562) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.852, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.848, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.861, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.851, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013452185471889906;, score=(train=0.858, test=0.545) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.856, test=0.569) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.866, test=0.570) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.857, test=0.562) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.852, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.848, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.857, test=0.584) total time= 0.4s [CV 7/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.861, test=0.551) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.851, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013452380952380948;, score=(train=0.858, test=0.545) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.855, test=0.569) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.865, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.856, test=0.562) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.852, test=0.555) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.848, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.860, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.851, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.842, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013468764503852211;, score=(train=0.858, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.000134694894146949;, score=(train=0.855, test=0.569) total time= 0.4s [CV 2/10] END ccp_alpha=0.000134694894146949;, score=(train=0.865, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.000134694894146949;, score=(train=0.856, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.000134694894146949;, score=(train=0.852, test=0.555) total time= 0.3s [CV 5/10] END ccp_alpha=0.000134694894146949;, score=(train=0.848, test=0.591) total time= 0.4s [CV 6/10] END ccp_alpha=0.000134694894146949;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.000134694894146949;, score=(train=0.860, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.000134694894146949;, score=(train=0.851, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.000134694894146949;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.000134694894146949;, score=(train=0.858, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.855, test=0.569) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.865, test=0.571) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.856, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.852, test=0.555) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.848, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.860, test=0.553) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.851, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.842, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001347368421052632;, score=(train=0.858, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.854, test=0.569) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.865, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.856, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.852, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.848, test=0.591) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.860, test=0.554) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.851, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013479853479853471;, score=(train=0.858, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.854, test=0.569) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.865, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.856, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.848, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.857, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.860, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.851, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013482726423902895;, score=(train=0.858, test=0.546) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.853, test=0.571) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.865, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.856, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.851, test=0.554) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.847, test=0.590) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.857, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.859, test=0.554) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.851, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001351576528047108;, score=(train=0.858, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.853, test=0.571) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.865, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.854, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.847, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.857, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.859, test=0.554) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.851, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013527950310559026;, score=(train=0.857, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.853, test=0.571) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.865, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.854, test=0.560) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.847, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.857, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.859, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.851, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.842, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001353158241170696;, score=(train=0.857, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.853, test=0.571) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.865, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.854, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.847, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.857, test=0.585) total time= 0.4s [CV 7/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.859, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.851, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.842, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013533834586466166;, score=(train=0.857, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.853, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.865, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.853, test=0.559) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.847, test=0.591) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.856, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.858, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.850, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.841, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001354497354497355;, score=(train=0.857, test=0.546) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.853, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.865, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.853, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.847, test=0.591) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.856, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.858, test=0.554) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.850, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.841, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013550183150183148;, score=(train=0.856, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.853, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.864, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.852, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.843, test=0.592) total time= 0.2s [CV 6/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.856, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.857, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.850, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.840, test=0.575) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013572371145194486;, score=(train=0.856, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.851, test=0.570) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.863, test=0.572) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.852, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.851, test=0.554) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.841, test=0.593) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.855, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.857, test=0.555) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.849, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.840, test=0.575) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013580315430819484;, score=(train=0.856, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.000135969868173258;, score=(train=0.851, test=0.570) total time= 0.4s [CV 2/10] END ccp_alpha=0.000135969868173258;, score=(train=0.863, test=0.573) total time= 0.3s [CV 3/10] END ccp_alpha=0.000135969868173258;, score=(train=0.851, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.000135969868173258;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.000135969868173258;, score=(train=0.841, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.000135969868173258;, score=(train=0.855, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.000135969868173258;, score=(train=0.857, test=0.555) total time= 0.4s [CV 8/10] END ccp_alpha=0.000135969868173258;, score=(train=0.849, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.000135969868173258;, score=(train=0.840, test=0.575) total time= 0.3s [CV 10/10] END ccp_alpha=0.000135969868173258;, score=(train=0.855, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.851, test=0.570) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.863, test=0.573) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.850, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.841, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.855, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.857, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.849, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.840, test=0.575) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013599758085052198;, score=(train=0.855, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.863, test=0.574) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.850, test=0.559) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.841, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.854, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.857, test=0.555) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.849, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013605442176870748;, score=(train=0.855, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.863, test=0.574) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.850, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.841, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.854, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.857, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.849, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013605442176870767;, score=(train=0.855, test=0.547) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.863, test=0.574) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.850, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.841, test=0.593) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.854, test=0.584) total time= 0.4s [CV 7/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.857, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.849, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013610133708655888;, score=(train=0.855, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.863, test=0.574) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.850, test=0.559) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.841, test=0.593) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.854, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.857, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.849, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.840, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013615578875822508;, score=(train=0.855, test=0.547) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.863, test=0.575) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.849, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.839, test=0.594) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.854, test=0.583) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.857, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.848, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001362571695344804;, score=(train=0.852, test=0.549) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.862, test=0.576) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.849, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.839, test=0.594) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.854, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.848, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001363969886493587;, score=(train=0.852, test=0.549) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.862, test=0.576) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.849, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.839, test=0.594) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.854, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.848, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013643880247134286;, score=(train=0.852, test=0.549) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.850, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.862, test=0.576) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.849, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.850, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.838, test=0.595) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.852, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.848, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013646616541353385;, score=(train=0.852, test=0.549) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.849, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.860, test=0.578) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.849, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.850, test=0.556) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.838, test=0.595) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.852, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.848, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.840, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013660357791029292;, score=(train=0.852, test=0.549) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.849, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.860, test=0.578) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.849, test=0.558) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.849, test=0.557) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.852, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.856, test=0.556) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.848, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.839, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013676609105180517;, score=(train=0.852, test=0.549) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.849, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.860, test=0.578) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.849, test=0.558) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.849, test=0.557) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.852, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.848, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.839, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013686446886446887;, score=(train=0.852, test=0.549) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.848, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.860, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.849, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.849, test=0.557) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.852, test=0.585) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.847, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.836, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001369834257981258;, score=(train=0.849, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.848, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.860, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.849, test=0.558) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.849, test=0.557) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.852, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.847, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.836, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013699855699855692;, score=(train=0.849, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.848, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.860, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.849, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.848, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.851, test=0.584) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.846, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.836, test=0.576) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001371428571428571;, score=(train=0.849, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.848, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.860, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.849, test=0.558) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.848, test=0.556) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.851, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.856, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.846, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.836, test=0.576) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013714285714285716;, score=(train=0.849, test=0.548) total time= 0.2s [CV 1/10] END ccp_alpha=0.000137375612080581;, score=(train=0.846, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.000137375612080581;, score=(train=0.858, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.000137375612080581;, score=(train=0.847, test=0.558) total time= 0.3s [CV 4/10] END ccp_alpha=0.000137375612080581;, score=(train=0.847, test=0.558) total time= 0.3s [CV 5/10] END ccp_alpha=0.000137375612080581;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.000137375612080581;, score=(train=0.850, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.000137375612080581;, score=(train=0.855, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.000137375612080581;, score=(train=0.846, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.000137375612080581;, score=(train=0.835, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.000137375612080581;, score=(train=0.848, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.845, test=0.572) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.858, test=0.577) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.846, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.847, test=0.558) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.850, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.854, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.846, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.835, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001375198412698413;, score=(train=0.848, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.845, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.858, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.846, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.846, test=0.558) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.837, test=0.596) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.849, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.854, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.846, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.835, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013766171701655601;, score=(train=0.847, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.844, test=0.573) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.858, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.846, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.846, test=0.558) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.837, test=0.596) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.849, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.854, test=0.555) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.846, test=0.578) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.835, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013779591836734698;, score=(train=0.847, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.843, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.858, test=0.578) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.845, test=0.559) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.846, test=0.558) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.836, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.849, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.853, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.846, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.835, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013806002316124697;, score=(train=0.847, test=0.548) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.842, test=0.574) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.856, test=0.576) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.844, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.845, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.836, test=0.597) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.848, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.853, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.846, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.834, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013844108366167597;, score=(train=0.846, test=0.550) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.842, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.856, test=0.576) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.844, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.845, test=0.561) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.836, test=0.597) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.848, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.852, test=0.556) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.846, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.834, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013848917770639888;, score=(train=0.846, test=0.550) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.842, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.856, test=0.575) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.844, test=0.560) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.845, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.835, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.848, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.852, test=0.557) total time= 0.4s [CV 8/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.844, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.834, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013869969040247667;, score=(train=0.845, test=0.551) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.842, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.856, test=0.575) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.844, test=0.560) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.845, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.835, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.848, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.852, test=0.557) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.844, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.834, test=0.578) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013872043944283385;, score=(train=0.845, test=0.551) total time= 0.4s [CV 1/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.841, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.856, test=0.575) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.844, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.845, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.835, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.847, test=0.587) total time= 0.4s [CV 7/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.851, test=0.557) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.844, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.834, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013881684981684986;, score=(train=0.845, test=0.551) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.841, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.855, test=0.576) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.843, test=0.560) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.845, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.835, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.847, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.851, test=0.557) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.843, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.834, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013897052154195007;, score=(train=0.844, test=0.551) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.841, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.854, test=0.574) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.843, test=0.560) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.844, test=0.559) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.835, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.847, test=0.587) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.851, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.843, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.834, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001390390938984289;, score=(train=0.844, test=0.551) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.841, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.853, test=0.575) total time= 0.4s [CV 3/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.843, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.842, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.834, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.846, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.851, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.843, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.833, test=0.578) total time= 0.4s [CV 10/10] END ccp_alpha=0.00013931972789115636;, score=(train=0.844, test=0.552) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.841, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.852, test=0.576) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.843, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.840, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.833, test=0.599) total time= 0.4s [CV 6/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.845, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.851, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.843, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.833, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013950906161763798;, score=(train=0.844, test=0.552) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.840, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.852, test=0.577) total time= 0.4s [CV 3/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.841, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.835, test=0.562) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.832, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.845, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.850, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.843, test=0.577) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.833, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013968253968253967;, score=(train=0.842, test=0.552) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.840, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.852, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.840, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.835, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.832, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.845, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.850, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.843, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.833, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013976956115779643;, score=(train=0.841, test=0.552) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.840, test=0.575) total time= 0.4s [CV 2/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.850, test=0.578) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.840, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.835, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.831, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.844, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.849, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.843, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.833, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013994708994708978;, score=(train=0.841, test=0.553) total time= 0.3s [CV 1/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.840, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.850, test=0.579) total time= 0.3s [CV 3/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.840, test=0.561) total time= 0.4s [CV 4/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.835, test=0.561) total time= 0.4s [CV 5/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.831, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.844, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.849, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.843, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.833, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00013997148391514593;, score=(train=0.841, test=0.553) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014;, score=(train=0.840, test=0.575) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014;, score=(train=0.850, test=0.579) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014;, score=(train=0.840, test=0.561) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014;, score=(train=0.835, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014;, score=(train=0.831, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014;, score=(train=0.844, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014;, score=(train=0.849, test=0.559) total time= 0.4s [CV 8/10] END ccp_alpha=0.00014;, score=(train=0.843, test=0.576) total time= 0.4s [CV 9/10] END ccp_alpha=0.00014;, score=(train=0.833, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014;, score=(train=0.841, test=0.553) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.840, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.850, test=0.579) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.840, test=0.561) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.834, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.830, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.844, test=0.589) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.849, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.842, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.833, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001400560224089634;, score=(train=0.840, test=0.553) total time= 0.4s [CV 1/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.839, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.849, test=0.579) total time= 0.4s [CV 3/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.839, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.834, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.830, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.844, test=0.589) total time= 0.4s [CV 7/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.849, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.842, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.832, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014043956043956044;, score=(train=0.839, test=0.553) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.839, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.849, test=0.581) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.839, test=0.562) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.834, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.830, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.844, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.849, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.842, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.832, test=0.578) total time= 0.4s [CV 10/10] END ccp_alpha=0.00014057959611400326;, score=(train=0.839, test=0.553) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.839, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.848, test=0.583) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.837, test=0.562) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.834, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.830, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.842, test=0.589) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.848, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.842, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.832, test=0.578) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014081704792203212;, score=(train=0.839, test=0.553) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.836, test=0.576) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.845, test=0.587) total time= 0.6s [CV 3/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.836, test=0.563) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.833, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.828, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.840, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.848, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.841, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.831, test=0.580) total time= 0.4s [CV 10/10] END ccp_alpha=0.00014126701185524708;, score=(train=0.837, test=0.554) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.836, test=0.576) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.845, test=0.587) total time= 0.4s [CV 3/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.836, test=0.563) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.833, test=0.560) total time= 0.4s [CV 5/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.828, test=0.599) total time= 0.4s [CV 6/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.840, test=0.586) total time= 0.4s [CV 7/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.848, test=0.559) total time= 0.4s [CV 8/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.841, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.831, test=0.580) total time= 0.4s [CV 10/10] END ccp_alpha=0.00014126938317414507;, score=(train=0.837, test=0.554) total time= 0.4s [CV 1/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.836, test=0.576) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.845, test=0.587) total time= 0.5s [CV 3/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.835, test=0.564) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.833, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.828, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.839, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.847, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.841, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.830, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014137291280148423;, score=(train=0.836, test=0.555) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.836, test=0.576) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.845, test=0.587) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.834, test=0.565) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.833, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.828, test=0.599) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.839, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.847, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.841, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.830, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001415057189464907;, score=(train=0.836, test=0.555) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.836, test=0.576) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.845, test=0.587) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.834, test=0.565) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.833, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.828, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.839, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.847, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.841, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.829, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014154698916603705;, score=(train=0.836, test=0.555) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.836, test=0.576) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.845, test=0.587) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.834, test=0.565) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.833, test=0.560) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.828, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.839, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.847, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.841, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.829, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001415563395820716;, score=(train=0.836, test=0.555) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.834, test=0.576) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.845, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.834, test=0.565) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.833, test=0.560) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.828, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.839, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.847, test=0.559) total time= 0.4s [CV 8/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.841, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.829, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014183150183150181;, score=(train=0.833, test=0.559) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.834, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.845, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.834, test=0.565) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.833, test=0.561) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.828, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.837, test=0.585) total time= 0.4s [CV 7/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.846, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.841, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.829, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014199713330148112;, score=(train=0.830, test=0.562) total time= 0.4s [CV 1/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.833, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.843, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.834, test=0.565) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.832, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.826, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.837, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.846, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.840, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.827, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014229918229918207;, score=(train=0.830, test=0.562) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.833, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.843, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.833, test=0.566) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.832, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.826, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.837, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.846, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.839, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.827, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014250216450216452;, score=(train=0.830, test=0.562) total time= 0.4s [CV 1/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.833, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.843, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.833, test=0.566) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.832, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.825, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.836, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.845, test=0.558) total time= 0.4s [CV 8/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.839, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.827, test=0.579) total time= 0.4s [CV 10/10] END ccp_alpha=0.00014269065520945224;, score=(train=0.830, test=0.562) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.832, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.842, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.832, test=0.565) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.832, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.825, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.836, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.845, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.838, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.827, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.830, test=0.562) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.832, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.842, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.831, test=0.565) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.831, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.825, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.836, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.845, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.837, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.827, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001429002267573696;, score=(train=0.829, test=0.561) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.832, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.841, test=0.587) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.831, test=0.565) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.831, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.825, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.836, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.845, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.837, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.827, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014298760552127246;, score=(train=0.828, test=0.562) total time= 0.4s [CV 1/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.830, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.841, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.830, test=0.565) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.831, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.825, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.834, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.844, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.836, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.827, test=0.579) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014338624338624327;, score=(train=0.828, test=0.563) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.830, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.840, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.830, test=0.565) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.831, test=0.564) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.825, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.832, test=0.587) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.844, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.836, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.826, test=0.580) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001434198334752146;, score=(train=0.827, test=0.563) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.830, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.840, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.830, test=0.565) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.831, test=0.564) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.825, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.832, test=0.588) total time= 0.4s [CV 7/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.844, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.836, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.826, test=0.580) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014356869184455398;, score=(train=0.826, test=0.567) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.830, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.840, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.829, test=0.564) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.831, test=0.564) total time= 0.4s [CV 5/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.825, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.832, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.844, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.836, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.826, test=0.580) total time= 0.4s [CV 10/10] END ccp_alpha=0.00014358974358974323;, score=(train=0.826, test=0.567) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.830, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.840, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.829, test=0.564) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.830, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.824, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.831, test=0.587) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.844, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.835, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.826, test=0.580) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001436940944987983;, score=(train=0.825, test=0.567) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.830, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.840, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.827, test=0.564) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.830, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.824, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.831, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.844, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.835, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.826, test=0.580) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014375503626107972;, score=(train=0.825, test=0.567) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.829, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.840, test=0.586) total time= 0.4s [CV 3/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.827, test=0.566) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.823, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.585) total time= 0.4s [CV 7/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.842, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.833, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.826, test=0.580) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.825, test=0.567) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.829, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.840, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.827, test=0.566) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.823, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.830, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.842, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.833, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.826, test=0.580) total time= 0.4s [CV 10/10] END ccp_alpha=0.00014394557823129258;, score=(train=0.825, test=0.567) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.827, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.838, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.829, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.600) total time= 0.4s [CV 6/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.829, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.841, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.833, test=0.575) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014455159703509034;, score=(train=0.823, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.827, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.838, test=0.586) total time= 0.4s [CV 3/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.829, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.823, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.829, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.841, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.832, test=0.575) total time= 0.4s [CV 9/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.822, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014460765506531465;, score=(train=0.823, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.826, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.838, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.829, test=0.562) total time= 0.4s [CV 5/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.823, test=0.600) total time= 0.4s [CV 6/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.827, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.841, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.830, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.822, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014465630426172872;, score=(train=0.823, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.000144701436130007;, score=(train=0.826, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.000144701436130007;, score=(train=0.838, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.000144701436130007;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.000144701436130007;, score=(train=0.829, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.000144701436130007;, score=(train=0.823, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.000144701436130007;, score=(train=0.827, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.000144701436130007;, score=(train=0.841, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.000144701436130007;, score=(train=0.830, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.000144701436130007;, score=(train=0.822, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.000144701436130007;, score=(train=0.823, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.826, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.838, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.829, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.823, test=0.600) total time= 0.4s [CV 6/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.827, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.841, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.830, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.822, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014470899470899474;, score=(train=0.823, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.826, test=0.574) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.838, test=0.586) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.829, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.823, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.827, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.841, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.830, test=0.573) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.822, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001447291447291447;, score=(train=0.823, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.824, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.838, test=0.587) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.828, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.821, test=0.599) total time= 0.4s [CV 6/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.826, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.839, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.829, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.820, test=0.580) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014512471655328795;, score=(train=0.823, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.824, test=0.573) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.837, test=0.589) total time= 0.4s [CV 3/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.827, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.821, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.825, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.838, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.828, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.819, test=0.581) total time= 0.4s [CV 10/10] END ccp_alpha=0.00014532445270191725;, score=(train=0.822, test=0.568) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.824, test=0.573) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.836, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.825, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.821, test=0.599) total time= 0.4s [CV 6/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.824, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.838, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.828, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.819, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014537519014519126;, score=(train=0.821, test=0.566) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.823, test=0.575) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.835, test=0.588) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.825, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.821, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.824, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.838, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.828, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.819, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014554313579995566;, score=(train=0.821, test=0.566) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.823, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.833, test=0.591) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.823, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.824, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.819, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.823, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.838, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.827, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.819, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001458503401360544;, score=(train=0.820, test=0.566) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.822, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.830, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.822, test=0.566) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.824, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.818, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.823, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.838, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.827, test=0.573) total time= 0.4s [CV 9/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.819, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014599415897071592;, score=(train=0.820, test=0.566) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.819, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.830, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.822, test=0.566) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.824, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.817, test=0.603) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.823, test=0.587) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.838, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.826, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.819, test=0.582) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001462545816552788;, score=(train=0.820, test=0.565) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.819, test=0.575) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.830, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.822, test=0.566) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.824, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.817, test=0.603) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.821, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.838, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.826, test=0.573) total time= 0.4s [CV 9/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.817, test=0.582) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014633634970083595;, score=(train=0.819, test=0.566) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.819, test=0.575) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.830, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.821, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.824, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.817, test=0.601) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.821, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.838, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.826, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.817, test=0.581) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001464566807019179;, score=(train=0.819, test=0.566) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.819, test=0.575) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.830, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.821, test=0.567) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.824, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.817, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.821, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.838, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.826, test=0.573) total time= 0.4s [CV 9/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.816, test=0.582) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014646760874321773;, score=(train=0.819, test=0.566) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.818, test=0.576) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.829, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.819, test=0.571) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.824, test=0.563) total time= 0.4s [CV 5/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.815, test=0.599) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.820, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.838, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.826, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.814, test=0.584) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014669372150679454;, score=(train=0.819, test=0.566) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.817, test=0.576) total time= 0.4s [CV 2/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.829, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.818, test=0.571) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.824, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.814, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.819, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.837, test=0.561) total time= 0.4s [CV 8/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.826, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.814, test=0.584) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014693877551020404;, score=(train=0.817, test=0.567) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.817, test=0.576) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.829, test=0.592) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.818, test=0.570) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.824, test=0.563) total time= 0.4s [CV 5/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.814, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.819, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.837, test=0.561) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.826, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.814, test=0.584) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014706851516978095;, score=(train=0.817, test=0.567) total time= 0.4s [CV 1/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.815, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.822, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.817, test=0.571) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.822, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.812, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.816, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.834, test=0.560) total time= 0.4s [CV 8/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.822, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.814, test=0.585) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014794679005205325;, score=(train=0.813, test=0.570) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.815, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.822, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.817, test=0.571) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.822, test=0.563) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.811, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.816, test=0.584) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.834, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.822, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.814, test=0.585) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001479500168861872;, score=(train=0.813, test=0.570) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.814, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.822, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.813, test=0.572) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.820, test=0.562) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.811, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.815, test=0.585) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.833, test=0.560) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.822, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.814, test=0.585) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001481142857142877;, score=(train=0.813, test=0.570) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.814, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.822, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.813, test=0.572) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.820, test=0.563) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.811, test=0.603) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.814, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.832, test=0.558) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.821, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.814, test=0.585) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014827838827838832;, score=(train=0.812, test=0.570) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.812, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.821, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.813, test=0.571) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.818, test=0.565) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.811, test=0.603) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.814, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.828, test=0.559) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.820, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.813, test=0.587) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001487242226372663;, score=(train=0.812, test=0.570) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.812, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.821, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.813, test=0.571) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.818, test=0.565) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.811, test=0.603) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.814, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.828, test=0.559) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.820, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.813, test=0.587) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001487288485764668;, score=(train=0.812, test=0.570) total time= 0.4s [CV 1/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.812, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.821, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.813, test=0.571) total time= 0.3s [CV 4/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.818, test=0.565) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.810, test=0.603) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.814, test=0.583) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.828, test=0.559) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.820, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.813, test=0.587) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014884285755311852;, score=(train=0.812, test=0.570) total time= 0.3s [CV 1/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.811, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.820, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.809, test=0.575) total time= 0.4s [CV 4/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.818, test=0.565) total time= 0.3s [CV 5/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.808, test=0.603) total time= 0.3s [CV 6/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.812, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.827, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.818, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.813, test=0.587) total time= 0.3s [CV 10/10] END ccp_alpha=0.00014936476126669457;, score=(train=0.812, test=0.570) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.811, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.820, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.809, test=0.575) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.817, test=0.566) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.808, test=0.603) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.812, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.827, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.818, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.813, test=0.587) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001494824016563147;, score=(train=0.812, test=0.570) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.810, test=0.578) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.818, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.807, test=0.576) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.817, test=0.565) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.806, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.811, test=0.586) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.826, test=0.560) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.816, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.810, test=0.587) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015012987012987021;, score=(train=0.810, test=0.572) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.807, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.814, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.805, test=0.576) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.812, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.803, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.807, test=0.589) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.819, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.815, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.803, test=0.596) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001514147891036417;, score=(train=0.799, test=0.577) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.807, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.814, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.805, test=0.576) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.812, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.803, test=0.600) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.807, test=0.589) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.819, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.815, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.803, test=0.596) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001514979467367969;, score=(train=0.799, test=0.577) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.805, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.812, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.804, test=0.576) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.812, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.801, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.804, test=0.587) total time= 0.5s [CV 7/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.818, test=0.562) total time= 0.4s [CV 8/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.815, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.802, test=0.597) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015212051292251777;, score=(train=0.798, test=0.576) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.805, test=0.580) total time= 0.4s [CV 2/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.811, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.803, test=0.576) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.812, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.801, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.803, test=0.588) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.818, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.814, test=0.577) total time= 0.4s [CV 9/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.802, test=0.597) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015226719323536888;, score=(train=0.798, test=0.576) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.805, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.810, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.802, test=0.576) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.810, test=0.566) total time= 0.4s [CV 5/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.800, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.800, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.817, test=0.563) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.814, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.801, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015238095238095237;, score=(train=0.798, test=0.577) total time= 0.4s [CV 1/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.805, test=0.580) total time= 0.4s [CV 2/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.809, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.802, test=0.576) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.810, test=0.566) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.800, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.800, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.817, test=0.563) total time= 0.4s [CV 8/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.814, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.801, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015247077471765392;, score=(train=0.797, test=0.578) total time= 0.3s [CV 1/10] END ccp_alpha=0.000152562358276644;, score=(train=0.804, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.000152562358276644;, score=(train=0.809, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.000152562358276644;, score=(train=0.802, test=0.576) total time= 0.3s [CV 4/10] END ccp_alpha=0.000152562358276644;, score=(train=0.810, test=0.566) total time= 0.4s [CV 5/10] END ccp_alpha=0.000152562358276644;, score=(train=0.800, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.000152562358276644;, score=(train=0.799, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.000152562358276644;, score=(train=0.817, test=0.563) total time= 0.3s [CV 8/10] END ccp_alpha=0.000152562358276644;, score=(train=0.814, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.000152562358276644;, score=(train=0.801, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.000152562358276644;, score=(train=0.797, test=0.578) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.799, test=0.579) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.803, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.802, test=0.575) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.810, test=0.566) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.800, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.799, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.817, test=0.563) total time= 0.4s [CV 8/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.814, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.801, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015279971846052752;, score=(train=0.796, test=0.578) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.799, test=0.579) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.803, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.802, test=0.575) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.810, test=0.566) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.800, test=0.602) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.799, test=0.590) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.817, test=0.562) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.814, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.801, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015290404040404045;, score=(train=0.795, test=0.578) total time= 0.4s [CV 1/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.798, test=0.580) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.802, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.801, test=0.576) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.810, test=0.566) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.794, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.799, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.814, test=0.562) total time= 0.4s [CV 8/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.814, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.801, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015336405529953884;, score=(train=0.795, test=0.579) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.797, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.802, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.793, test=0.577) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.809, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.794, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.799, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.812, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.809, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.794, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001540293040293042;, score=(train=0.795, test=0.579) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.797, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.802, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.793, test=0.577) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.809, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.794, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.799, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.812, test=0.565) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.809, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.794, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001540616246498601;, score=(train=0.795, test=0.579) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.797, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.801, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.793, test=0.577) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.809, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.794, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.799, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.812, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.809, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.794, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015413978884298493;, score=(train=0.795, test=0.579) total time= 0.4s [CV 1/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.797, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.801, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.793, test=0.577) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.809, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.794, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.799, test=0.590) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.812, test=0.565) total time= 0.4s [CV 8/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.809, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.794, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015422374364118573;, score=(train=0.795, test=0.579) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.797, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.801, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.577) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.799, test=0.590) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.811, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.794, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.795, test=0.579) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.797, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.801, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.577) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.793, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.799, test=0.590) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.811, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.809, test=0.572) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.794, test=0.599) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015428571428571428;, score=(train=0.795, test=0.579) total time= 0.4s [CV 1/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.796, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.801, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.793, test=0.577) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.809, test=0.567) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.793, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.793, test=0.592) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.811, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.808, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.794, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015436853002070392;, score=(train=0.795, test=0.579) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.796, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.800, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.577) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.808, test=0.569) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.592) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.811, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.808, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.793, test=0.597) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015442437511403023;, score=(train=0.795, test=0.579) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.796, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.797, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.792, test=0.577) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.807, test=0.571) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.792, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.793, test=0.593) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.811, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.808, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.793, test=0.597) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015452886420628327;, score=(train=0.791, test=0.581) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.796, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.797, test=0.593) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.792, test=0.577) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.807, test=0.571) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.792, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.793, test=0.593) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.810, test=0.565) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.808, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.789, test=0.600) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001546151092015754;, score=(train=0.791, test=0.581) total time= 0.3s [CV 1/10] END ccp_alpha=0.000154681389463998;, score=(train=0.796, test=0.581) total time= 0.3s [CV 2/10] END ccp_alpha=0.000154681389463998;, score=(train=0.797, test=0.593) total time= 0.4s [CV 3/10] END ccp_alpha=0.000154681389463998;, score=(train=0.789, test=0.581) total time= 0.4s [CV 4/10] END ccp_alpha=0.000154681389463998;, score=(train=0.807, test=0.571) total time= 0.3s [CV 5/10] END ccp_alpha=0.000154681389463998;, score=(train=0.792, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.000154681389463998;, score=(train=0.793, test=0.593) total time= 0.3s [CV 7/10] END ccp_alpha=0.000154681389463998;, score=(train=0.810, test=0.566) total time= 0.3s [CV 8/10] END ccp_alpha=0.000154681389463998;, score=(train=0.808, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.000154681389463998;, score=(train=0.789, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.000154681389463998;, score=(train=0.791, test=0.581) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.794, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.795, test=0.593) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.787, test=0.582) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.807, test=0.571) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.791, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.792, test=0.593) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.810, test=0.566) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.808, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.789, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015492742551566092;, score=(train=0.790, test=0.582) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.793, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.795, test=0.594) total time= 0.4s [CV 3/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.786, test=0.580) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.807, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.790, test=0.600) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.792, test=0.593) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.809, test=0.568) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.808, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.789, test=0.599) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015522077922077923;, score=(train=0.788, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.793, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.795, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.785, test=0.581) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.807, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.790, test=0.600) total time= 0.4s [CV 6/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.792, test=0.593) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.809, test=0.568) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.808, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.789, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015529356270494832;, score=(train=0.788, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.793, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.794, test=0.594) total time= 0.4s [CV 3/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.784, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.806, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.790, test=0.600) total time= 0.4s [CV 6/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.790, test=0.594) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.807, test=0.567) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.803, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.788, test=0.599) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015555555555555562;, score=(train=0.788, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.793, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.794, test=0.594) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.784, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.806, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.789, test=0.599) total time= 0.4s [CV 6/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.789, test=0.595) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.807, test=0.567) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.803, test=0.573) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.788, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015576118646216247;, score=(train=0.787, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.000155809896015074;, score=(train=0.793, test=0.583) total time= 0.4s [CV 2/10] END ccp_alpha=0.000155809896015074;, score=(train=0.794, test=0.594) total time= 0.4s [CV 3/10] END ccp_alpha=0.000155809896015074;, score=(train=0.784, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.000155809896015074;, score=(train=0.806, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.000155809896015074;, score=(train=0.789, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.000155809896015074;, score=(train=0.789, test=0.595) total time= 0.3s [CV 7/10] END ccp_alpha=0.000155809896015074;, score=(train=0.807, test=0.567) total time= 0.3s [CV 8/10] END ccp_alpha=0.000155809896015074;, score=(train=0.803, test=0.573) total time= 0.4s [CV 9/10] END ccp_alpha=0.000155809896015074;, score=(train=0.788, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.000155809896015074;, score=(train=0.787, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.791, test=0.583) total time= 0.4s [CV 2/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.794, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.783, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.806, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.788, test=0.598) total time= 0.4s [CV 6/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.789, test=0.595) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.806, test=0.567) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.803, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.787, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015608875401321165;, score=(train=0.787, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.791, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.794, test=0.595) total time= 0.4s [CV 3/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.783, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.806, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.788, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.788, test=0.595) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.806, test=0.567) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.803, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.787, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015612558869701728;, score=(train=0.787, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.791, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.794, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.783, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.806, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.788, test=0.598) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.788, test=0.595) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.806, test=0.567) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.803, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.787, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001561425688114083;, score=(train=0.787, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.791, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.792, test=0.597) total time= 0.4s [CV 3/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.783, test=0.584) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.804, test=0.573) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.788, test=0.598) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.788, test=0.596) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.805, test=0.568) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.803, test=0.574) total time= 0.4s [CV 9/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.786, test=0.600) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015639184340630264;, score=(train=0.786, test=0.579) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.791, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.792, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.783, test=0.584) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.804, test=0.573) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.788, test=0.598) total time= 0.4s [CV 6/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.787, test=0.595) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.801, test=0.569) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.803, test=0.574) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.786, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015657291032012126;, score=(train=0.782, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.790, test=0.585) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.792, test=0.598) total time= 0.4s [CV 3/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.783, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.804, test=0.573) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.783, test=0.597) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.786, test=0.596) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.801, test=0.570) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.801, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.785, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015703502445012555;, score=(train=0.782, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.785, test=0.583) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.786, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.781, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.802, test=0.574) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.781, test=0.601) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.783, test=0.597) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.799, test=0.570) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.799, test=0.576) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.779, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015777893217893226;, score=(train=0.779, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.783, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.778, test=0.597) total time= 0.4s [CV 3/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.775, test=0.585) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.801, test=0.573) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.776, test=0.604) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.781, test=0.598) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.795, test=0.573) total time= 0.4s [CV 8/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.795, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.779, test=0.599) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015860903367558195;, score=(train=0.777, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.783, test=0.582) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.776, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.775, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.799, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.775, test=0.604) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.780, test=0.598) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.794, test=0.574) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.795, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.779, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001587666065278006;, score=(train=0.776, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.779, test=0.584) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.776, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.771, test=0.588) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.796, test=0.566) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.775, test=0.604) total time= 0.4s [CV 6/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.776, test=0.599) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.794, test=0.574) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.795, test=0.579) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.779, test=0.599) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015922158337019026;, score=(train=0.776, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.779, test=0.584) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.776, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.771, test=0.588) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.795, test=0.569) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.774, test=0.605) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.776, test=0.600) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.794, test=0.573) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.791, test=0.580) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.779, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015952615528970205;, score=(train=0.775, test=0.580) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.779, test=0.584) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.775, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.770, test=0.589) total time= 0.4s [CV 4/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.795, test=0.569) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.774, test=0.605) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.776, test=0.600) total time= 0.3s [CV 7/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.794, test=0.573) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.790, test=0.577) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.777, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.00015979336939526577;, score=(train=0.772, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.779, test=0.584) total time= 0.3s [CV 2/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.775, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.770, test=0.589) total time= 0.3s [CV 4/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.795, test=0.569) total time= 0.3s [CV 5/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.773, test=0.607) total time= 0.3s [CV 6/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.776, test=0.600) total time= 0.4s [CV 7/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.792, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.789, test=0.579) total time= 0.3s [CV 9/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.777, test=0.600) total time= 0.3s [CV 10/10] END ccp_alpha=0.00015999999999999999;, score=(train=0.772, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.779, test=0.584) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.775, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.770, test=0.589) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.795, test=0.569) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.773, test=0.607) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.776, test=0.600) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.792, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.789, test=0.579) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.777, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.00016004527447651394;, score=(train=0.772, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.779, test=0.584) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.775, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.770, test=0.589) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.795, test=0.569) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.773, test=0.607) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.776, test=0.600) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.792, test=0.574) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.789, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.775, test=0.601) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016008659002574727;, score=(train=0.772, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.584) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.769, test=0.589) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.795, test=0.569) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.773, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.601) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.792, test=0.574) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.789, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.775, test=0.601) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001602361218085636;, score=(train=0.771, test=0.582) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.773, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.773, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.769, test=0.589) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.793, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.773, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.774, test=0.600) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.790, test=0.573) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.788, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.774, test=0.600) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016088888888888888;, score=(train=0.770, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.768, test=0.589) total time= 0.4s [CV 4/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.792, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.773, test=0.600) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.790, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.788, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.772, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.00016117763972128482;, score=(train=0.769, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.768, test=0.589) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.792, test=0.570) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.773, test=0.600) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.790, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.788, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.772, test=0.600) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001612244897959184;, score=(train=0.768, test=0.583) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.772, test=0.587) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.771, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.767, test=0.588) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.789, test=0.574) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.772, test=0.608) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.772, test=0.601) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.789, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.787, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.771, test=0.601) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001620606900273458;, score=(train=0.763, test=0.584) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.772, test=0.587) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.771, test=0.599) total time= 0.4s [CV 3/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.767, test=0.588) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.789, test=0.574) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.772, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.771, test=0.601) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.789, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.787, test=0.578) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.771, test=0.601) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016213273780445354;, score=(train=0.763, test=0.584) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.771, test=0.587) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.766, test=0.598) total time= 0.4s [CV 3/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.764, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.786, test=0.576) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.771, test=0.607) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.770, test=0.603) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.784, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.779, test=0.581) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.770, test=0.600) total time= 0.4s [CV 10/10] END ccp_alpha=0.00016326530612244898;, score=(train=0.762, test=0.587) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.771, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.765, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.764, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.785, test=0.576) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.770, test=0.608) total time= 0.4s [CV 6/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.768, test=0.603) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.784, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.778, test=0.580) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.769, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016363269762053288;, score=(train=0.762, test=0.587) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.771, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.765, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.764, test=0.585) total time= 0.4s [CV 4/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.785, test=0.576) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.770, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.768, test=0.603) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.784, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.778, test=0.580) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.769, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016363636363636374;, score=(train=0.762, test=0.587) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.770, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.764, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.763, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.784, test=0.575) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.770, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.767, test=0.605) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.781, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.778, test=0.580) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.769, test=0.599) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016383495145631073;, score=(train=0.761, test=0.590) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.770, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.764, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.763, test=0.585) total time= 0.4s [CV 4/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.783, test=0.577) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.770, test=0.610) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.767, test=0.605) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.781, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.778, test=0.581) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.769, test=0.599) total time= 0.4s [CV 10/10] END ccp_alpha=0.00016415308829101924;, score=(train=0.761, test=0.590) total time= 0.4s [CV 1/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.770, test=0.586) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.762, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.763, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.782, test=0.575) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.769, test=0.609) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.766, test=0.603) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.781, test=0.576) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.776, test=0.584) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.767, test=0.600) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016470417100911475;, score=(train=0.761, test=0.591) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.769, test=0.587) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.760, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.761, test=0.585) total time= 0.4s [CV 4/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.773, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.766, test=0.610) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.765, test=0.602) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.780, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.770, test=0.585) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.765, test=0.605) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016574679995285212;, score=(train=0.760, test=0.591) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.769, test=0.587) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.759, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.761, test=0.585) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.773, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.766, test=0.610) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.765, test=0.602) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.780, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.770, test=0.585) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.765, test=0.605) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016584052427846786;, score=(train=0.760, test=0.591) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.767, test=0.588) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.758, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.760, test=0.584) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.771, test=0.581) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.765, test=0.608) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.765, test=0.602) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.780, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.769, test=0.585) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.761, test=0.603) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001663221835635629;, score=(train=0.760, test=0.590) total time= 0.4s [CV 1/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.761, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.755, test=0.602) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.760, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.771, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.764, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.763, test=0.603) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.779, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.768, test=0.587) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.760, test=0.603) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016694324035682745;, score=(train=0.760, test=0.590) total time= 0.4s [CV 1/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.761, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.755, test=0.602) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.760, test=0.583) total time= 0.4s [CV 4/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.771, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.764, test=0.608) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.763, test=0.603) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.779, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.768, test=0.587) total time= 0.4s [CV 9/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.760, test=0.603) total time= 0.4s [CV 10/10] END ccp_alpha=0.00016709216709216734;, score=(train=0.759, test=0.589) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.761, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.753, test=0.603) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.760, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.770, test=0.579) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.763, test=0.609) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.763, test=0.603) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.779, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.768, test=0.587) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.759, test=0.606) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016727272727272706;, score=(train=0.759, test=0.589) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.761, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.753, test=0.603) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.760, test=0.583) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.770, test=0.579) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.763, test=0.609) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.763, test=0.603) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.779, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.768, test=0.587) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.759, test=0.606) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001673173884938592;, score=(train=0.759, test=0.589) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.761, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.753, test=0.603) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.760, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.770, test=0.579) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.762, test=0.609) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.763, test=0.603) total time= 0.4s [CV 7/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.779, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.768, test=0.588) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.759, test=0.606) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016755009696186164;, score=(train=0.759, test=0.589) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.759, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.753, test=0.603) total time= 0.4s [CV 3/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.760, test=0.583) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.770, test=0.579) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.762, test=0.609) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.763, test=0.603) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.779, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.768, test=0.588) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.757, test=0.604) total time= 0.4s [CV 10/10] END ccp_alpha=0.00016760873700474285;, score=(train=0.759, test=0.589) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.759, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.753, test=0.603) total time= 0.3s [CV 3/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.759, test=0.584) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.770, test=0.579) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.762, test=0.609) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.763, test=0.603) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.779, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.768, test=0.588) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.757, test=0.604) total time= 0.3s [CV 10/10] END ccp_alpha=0.00016762602476888197;, score=(train=0.759, test=0.589) total time= 0.3s [CV 1/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.753, test=0.594) total time= 0.3s [CV 2/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.748, test=0.601) total time= 0.4s [CV 3/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.751, test=0.594) total time= 0.3s [CV 4/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.761, test=0.584) total time= 0.3s [CV 5/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.754, test=0.606) total time= 0.3s [CV 6/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.756, test=0.608) total time= 0.3s [CV 7/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.771, test=0.574) total time= 0.3s [CV 8/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.758, test=0.592) total time= 0.3s [CV 9/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.751, test=0.610) total time= 0.4s [CV 10/10] END ccp_alpha=0.00016994004563029028;, score=(train=0.755, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.752, test=0.592) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.748, test=0.601) total time= 0.4s [CV 3/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.751, test=0.594) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.761, test=0.583) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.754, test=0.606) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.756, test=0.608) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.770, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.758, test=0.592) total time= 0.4s [CV 9/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.751, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017003841214746407;, score=(train=0.755, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.752, test=0.592) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.747, test=0.600) total time= 0.4s [CV 3/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.751, test=0.594) total time= 0.4s [CV 4/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.761, test=0.583) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.754, test=0.606) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.756, test=0.608) total time= 0.3s [CV 7/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.769, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.758, test=0.592) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.751, test=0.610) total time= 0.4s [CV 10/10] END ccp_alpha=0.00017024697730381837;, score=(train=0.755, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.747, test=0.591) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.747, test=0.601) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.750, test=0.593) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.757, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.754, test=0.606) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.755, test=0.607) total time= 0.3s [CV 7/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.768, test=0.579) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.758, test=0.592) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.751, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017073438853076536;, score=(train=0.754, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.747, test=0.601) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.750, test=0.593) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.757, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.753, test=0.607) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.755, test=0.607) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.767, test=0.579) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.758, test=0.592) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.751, test=0.610) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001709010989010989;, score=(train=0.754, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.747, test=0.601) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.750, test=0.593) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.757, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.752, test=0.609) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.755, test=0.607) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.767, test=0.578) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.758, test=0.592) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.751, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017120557101202015;, score=(train=0.754, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.747, test=0.601) total time= 0.4s [CV 3/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.750, test=0.593) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.757, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.752, test=0.609) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.755, test=0.607) total time= 0.3s [CV 7/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.767, test=0.578) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.758, test=0.592) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.751, test=0.610) total time= 0.4s [CV 10/10] END ccp_alpha=0.00017123728123728146;, score=(train=0.753, test=0.592) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.747, test=0.601) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.750, test=0.593) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.757, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.752, test=0.609) total time= 0.4s [CV 6/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.755, test=0.607) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.767, test=0.578) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.758, test=0.592) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.751, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017127115617764956;, score=(train=0.753, test=0.592) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.746, test=0.600) total time= 0.4s [CV 3/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.749, test=0.592) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.755, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.749, test=0.610) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.755, test=0.608) total time= 0.3s [CV 7/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.767, test=0.578) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.758, test=0.591) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.751, test=0.610) total time= 0.4s [CV 10/10] END ccp_alpha=0.00017145770209220975;, score=(train=0.752, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.746, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.749, test=0.592) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.754, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.748, test=0.610) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.754, test=0.608) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.767, test=0.578) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.757, test=0.590) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.751, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017183520738124625;, score=(train=0.751, test=0.593) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.737, test=0.597) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.748, test=0.593) total time= 0.4s [CV 4/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.754, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.747, test=0.612) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.752, test=0.608) total time= 0.3s [CV 7/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.762, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.754, test=0.597) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.750, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017244364830571726;, score=(train=0.750, test=0.594) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.746, test=0.593) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.737, test=0.597) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.746, test=0.596) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.754, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.747, test=0.612) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.752, test=0.608) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.762, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.754, test=0.597) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.749, test=0.609) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017269841269841255;, score=(train=0.750, test=0.594) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.737, test=0.597) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.733, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.746, test=0.596) total time= 0.4s [CV 4/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.754, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.742, test=0.617) total time= 0.4s [CV 6/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.750, test=0.609) total time= 0.3s [CV 7/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.759, test=0.576) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.753, test=0.598) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.749, test=0.609) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017365682952213447;, score=(train=0.745, test=0.596) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.737, test=0.597) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.733, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.746, test=0.596) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.754, test=0.580) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.742, test=0.617) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.750, test=0.609) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.757, test=0.578) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.753, test=0.599) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.749, test=0.609) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017387241689128486;, score=(train=0.745, test=0.596) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.731, test=0.597) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.733, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.743, test=0.599) total time= 0.4s [CV 4/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.751, test=0.582) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.738, test=0.620) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.749, test=0.609) total time= 0.3s [CV 7/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.754, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.751, test=0.598) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.748, test=0.609) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017538461538461544;, score=(train=0.738, test=0.599) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.731, test=0.597) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.733, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.741, test=0.598) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.748, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.736, test=0.620) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.748, test=0.609) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.753, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.749, test=0.599) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.748, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017568627450980392;, score=(train=0.737, test=0.599) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.731, test=0.597) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.733, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.741, test=0.598) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.748, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.736, test=0.620) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.748, test=0.609) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.753, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.749, test=0.599) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.747, test=0.609) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001758014316837846;, score=(train=0.737, test=0.599) total time= 0.3s [CV 1/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.731, test=0.597) total time= 0.3s [CV 2/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.733, test=0.600) total time= 0.4s [CV 3/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.740, test=0.601) total time= 0.3s [CV 4/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.747, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.736, test=0.619) total time= 0.3s [CV 6/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.745, test=0.614) total time= 0.4s [CV 7/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.752, test=0.575) total time= 0.3s [CV 8/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.747, test=0.600) total time= 0.3s [CV 9/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.747, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.00017610940106923875;, score=(train=0.734, test=0.601) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.731, test=0.597) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.733, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.739, test=0.600) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.747, test=0.585) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.736, test=0.619) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.745, test=0.614) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.751, test=0.577) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.747, test=0.600) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.745, test=0.612) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001764281008073606;, score=(train=0.734, test=0.601) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.726, test=0.604) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.730, test=0.601) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.735, test=0.604) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.746, test=0.586) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.728, test=0.620) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.741, test=0.615) total time= 0.4s [CV 7/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.748, test=0.580) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.741, test=0.599) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.739, test=0.615) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001785637891520245;, score=(train=0.731, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.725, test=0.606) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.728, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.734, test=0.606) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.744, test=0.587) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.728, test=0.621) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.741, test=0.615) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.748, test=0.580) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.740, test=0.600) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.738, test=0.615) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001791888304931784;, score=(train=0.729, test=0.603) total time= 0.4s [CV 1/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.720, test=0.609) total time= 0.3s [CV 2/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.726, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.734, test=0.606) total time= 0.3s [CV 4/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.743, test=0.588) total time= 0.4s [CV 5/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.725, test=0.623) total time= 0.3s [CV 6/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.740, test=0.617) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.746, test=0.580) total time= 0.3s [CV 8/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.736, test=0.605) total time= 0.3s [CV 9/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.738, test=0.615) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018021905108687474;, score=(train=0.729, test=0.604) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.720, test=0.609) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.726, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.731, test=0.610) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.742, test=0.589) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.724, test=0.625) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.739, test=0.617) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.745, test=0.582) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.736, test=0.605) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.737, test=0.615) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001804271783084124;, score=(train=0.729, test=0.604) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.720, test=0.609) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.726, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.728, test=0.611) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.741, test=0.588) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.723, test=0.624) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.739, test=0.617) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.743, test=0.582) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.735, test=0.605) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.734, test=0.616) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001806802721088434;, score=(train=0.728, test=0.605) total time= 0.3s [CV 1/10] END ccp_alpha=0.000181037272316907;, score=(train=0.719, test=0.609) total time= 0.3s [CV 2/10] END ccp_alpha=0.000181037272316907;, score=(train=0.725, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.000181037272316907;, score=(train=0.726, test=0.612) total time= 0.4s [CV 4/10] END ccp_alpha=0.000181037272316907;, score=(train=0.741, test=0.588) total time= 0.3s [CV 5/10] END ccp_alpha=0.000181037272316907;, score=(train=0.721, test=0.625) total time= 0.3s [CV 6/10] END ccp_alpha=0.000181037272316907;, score=(train=0.739, test=0.617) total time= 0.3s [CV 7/10] END ccp_alpha=0.000181037272316907;, score=(train=0.743, test=0.582) total time= 0.3s [CV 8/10] END ccp_alpha=0.000181037272316907;, score=(train=0.735, test=0.605) total time= 0.3s [CV 9/10] END ccp_alpha=0.000181037272316907;, score=(train=0.734, test=0.616) total time= 0.3s [CV 10/10] END ccp_alpha=0.000181037272316907;, score=(train=0.728, test=0.605) total time= 0.4s [CV 1/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.719, test=0.609) total time= 0.3s [CV 2/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.725, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.726, test=0.612) total time= 0.3s [CV 4/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.740, test=0.588) total time= 0.3s [CV 5/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.720, test=0.624) total time= 0.3s [CV 6/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.739, test=0.617) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.743, test=0.582) total time= 0.4s [CV 8/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.734, test=0.607) total time= 0.3s [CV 9/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.734, test=0.616) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018132969276038469;, score=(train=0.728, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.718, test=0.611) total time= 0.3s [CV 2/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.723, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.724, test=0.613) total time= 0.3s [CV 4/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.739, test=0.588) total time= 0.4s [CV 5/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.717, test=0.624) total time= 0.3s [CV 6/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.736, test=0.620) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.743, test=0.581) total time= 0.3s [CV 8/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.734, test=0.607) total time= 0.3s [CV 9/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.734, test=0.618) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018205414760907477;, score=(train=0.728, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.718, test=0.612) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.723, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.724, test=0.613) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.737, test=0.593) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.717, test=0.624) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.735, test=0.621) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.743, test=0.581) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.731, test=0.606) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.732, test=0.619) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001824964161317727;, score=(train=0.726, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.717, test=0.611) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.723, test=0.596) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.724, test=0.613) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.731, test=0.593) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.716, test=0.628) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.735, test=0.621) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.743, test=0.581) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.731, test=0.606) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.732, test=0.618) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001827366667441349;, score=(train=0.726, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.709, test=0.613) total time= 0.4s [CV 2/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.716, test=0.597) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.719, test=0.613) total time= 0.4s [CV 4/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.716, test=0.606) total time= 0.3s [CV 5/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.710, test=0.629) total time= 0.3s [CV 6/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.726, test=0.619) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.732, test=0.591) total time= 0.3s [CV 8/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.724, test=0.613) total time= 0.4s [CV 9/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.725, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018640904375836652;, score=(train=0.714, test=0.607) total time= 0.3s [CV 1/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.709, test=0.613) total time= 0.3s [CV 2/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.714, test=0.600) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.719, test=0.613) total time= 0.4s [CV 4/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.716, test=0.606) total time= 0.3s [CV 5/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.709, test=0.629) total time= 0.3s [CV 6/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.726, test=0.620) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.732, test=0.591) total time= 0.3s [CV 8/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.724, test=0.613) total time= 0.3s [CV 9/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.725, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018674772036474368;, score=(train=0.713, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.709, test=0.613) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.714, test=0.600) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.719, test=0.613) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.716, test=0.606) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.709, test=0.629) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.726, test=0.620) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.732, test=0.591) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.724, test=0.613) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.725, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001867489653203931;, score=(train=0.713, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.708, test=0.613) total time= 0.3s [CV 2/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.710, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.709, test=0.614) total time= 0.3s [CV 4/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.714, test=0.604) total time= 0.3s [CV 5/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.707, test=0.626) total time= 0.4s [CV 6/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.726, test=0.619) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.730, test=0.593) total time= 0.4s [CV 8/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.720, test=0.612) total time= 0.3s [CV 9/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.724, test=0.619) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018824344559817408;, score=(train=0.710, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.708, test=0.613) total time= 0.4s [CV 2/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.710, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.709, test=0.614) total time= 0.3s [CV 4/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.713, test=0.604) total time= 0.3s [CV 5/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.707, test=0.626) total time= 0.3s [CV 6/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.723, test=0.619) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.730, test=0.593) total time= 0.3s [CV 8/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.720, test=0.612) total time= 0.4s [CV 9/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.724, test=0.619) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018832759316177172;, score=(train=0.710, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.708, test=0.613) total time= 0.4s [CV 2/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.710, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.709, test=0.614) total time= 0.3s [CV 4/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.713, test=0.604) total time= 0.3s [CV 5/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.707, test=0.626) total time= 0.4s [CV 6/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.723, test=0.619) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.730, test=0.593) total time= 0.3s [CV 8/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.720, test=0.612) total time= 0.3s [CV 9/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.723, test=0.619) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018849470802715997;, score=(train=0.710, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.707, test=0.614) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.710, test=0.598) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.709, test=0.615) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.713, test=0.604) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.698, test=0.629) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.723, test=0.619) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.730, test=0.593) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.719, test=0.613) total time= 0.4s [CV 9/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.723, test=0.619) total time= 0.4s [CV 10/10] END ccp_alpha=0.0001892931085477668;, score=(train=0.710, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.707, test=0.614) total time= 0.3s [CV 2/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.710, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.709, test=0.615) total time= 0.3s [CV 4/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.713, test=0.604) total time= 0.3s [CV 5/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.698, test=0.629) total time= 0.4s [CV 6/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.723, test=0.619) total time= 0.3s [CV 7/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.729, test=0.592) total time= 0.3s [CV 8/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.719, test=0.613) total time= 0.3s [CV 9/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.723, test=0.619) total time= 0.3s [CV 10/10] END ccp_alpha=0.00018952567642835285;, score=(train=0.710, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.706, test=0.615) total time= 0.3s [CV 2/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.708, test=0.598) total time= 0.4s [CV 3/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.707, test=0.616) total time= 0.3s [CV 4/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.710, test=0.605) total time= 0.3s [CV 5/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.696, test=0.631) total time= 0.3s [CV 6/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.718, test=0.621) total time= 0.3s [CV 7/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.728, test=0.591) total time= 0.4s [CV 8/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.712, test=0.613) total time= 0.3s [CV 9/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.722, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00019126373626373607;, score=(train=0.709, test=0.601) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.706, test=0.615) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.708, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.707, test=0.616) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.710, test=0.605) total time= 0.4s [CV 5/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.696, test=0.631) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.718, test=0.621) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.728, test=0.591) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.712, test=0.613) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.722, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001914846544586624;, score=(train=0.708, test=0.603) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.706, test=0.615) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.707, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.706, test=0.616) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.710, test=0.606) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.693, test=0.636) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.708, test=0.625) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.726, test=0.592) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.712, test=0.613) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.722, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001926674976073569;, score=(train=0.708, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.706, test=0.615) total time= 0.3s [CV 2/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.707, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.706, test=0.617) total time= 0.3s [CV 4/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.709, test=0.607) total time= 0.4s [CV 5/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.692, test=0.635) total time= 0.3s [CV 6/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.708, test=0.625) total time= 0.3s [CV 7/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.726, test=0.592) total time= 0.3s [CV 8/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.712, test=0.612) total time= 0.3s [CV 9/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.722, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00019290423861852406;, score=(train=0.708, test=0.603) total time= 0.4s [CV 1/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.706, test=0.615) total time= 0.4s [CV 2/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.707, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.706, test=0.617) total time= 0.4s [CV 4/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.707, test=0.609) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.690, test=0.636) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.708, test=0.625) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.726, test=0.592) total time= 0.4s [CV 8/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.712, test=0.612) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.721, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001930611365722421;, score=(train=0.708, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.703, test=0.616) total time= 0.3s [CV 2/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.706, test=0.598) total time= 0.3s [CV 3/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.706, test=0.617) total time= 0.3s [CV 4/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.707, test=0.609) total time= 0.4s [CV 5/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.690, test=0.636) total time= 0.3s [CV 6/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.708, test=0.625) total time= 0.3s [CV 7/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.725, test=0.593) total time= 0.4s [CV 8/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.712, test=0.612) total time= 0.3s [CV 9/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.721, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00019336989380611832;, score=(train=0.708, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.703, test=0.615) total time= 0.4s [CV 2/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.705, test=0.599) total time= 0.3s [CV 3/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.702, test=0.616) total time= 0.3s [CV 4/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.704, test=0.609) total time= 0.3s [CV 5/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.689, test=0.636) total time= 0.3s [CV 6/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.706, test=0.627) total time= 0.3s [CV 7/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.724, test=0.593) total time= 0.3s [CV 8/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.710, test=0.613) total time= 0.4s [CV 9/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.719, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00019451462988357449;, score=(train=0.707, test=0.604) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.703, test=0.615) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.700, test=0.604) total time= 0.3s [CV 3/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.702, test=0.616) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.703, test=0.610) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.688, test=0.637) total time= 0.4s [CV 6/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.703, test=0.629) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.722, test=0.594) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.708, test=0.615) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.719, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001959420289855072;, score=(train=0.705, test=0.605) total time= 0.3s [CV 1/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.699, test=0.616) total time= 0.4s [CV 2/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.699, test=0.604) total time= 0.3s [CV 3/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.702, test=0.616) total time= 0.4s [CV 4/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.703, test=0.610) total time= 0.3s [CV 5/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.687, test=0.638) total time= 0.3s [CV 6/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.702, test=0.629) total time= 0.3s [CV 7/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.719, test=0.591) total time= 0.3s [CV 8/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.708, test=0.615) total time= 0.4s [CV 9/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.718, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00019676375404530723;, score=(train=0.704, test=0.604) total time= 0.3s [CV 1/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.699, test=0.616) total time= 0.3s [CV 2/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.697, test=0.601) total time= 0.3s [CV 3/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.701, test=0.618) total time= 0.3s [CV 4/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.703, test=0.610) total time= 0.4s [CV 5/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.686, test=0.638) total time= 0.4s [CV 6/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.698, test=0.628) total time= 0.3s [CV 7/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.718, test=0.591) total time= 0.3s [CV 8/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.708, test=0.615) total time= 0.3s [CV 9/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.717, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00019809230252545532;, score=(train=0.702, test=0.607) total time= 0.3s [CV 1/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.699, test=0.616) total time= 0.3s [CV 2/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.697, test=0.601) total time= 0.4s [CV 3/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.701, test=0.618) total time= 0.3s [CV 4/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.702, test=0.611) total time= 0.3s [CV 5/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.686, test=0.638) total time= 0.3s [CV 6/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.698, test=0.628) total time= 0.3s [CV 7/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.718, test=0.591) total time= 0.3s [CV 8/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.708, test=0.616) total time= 0.3s [CV 9/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.716, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.0001984268016753804;, score=(train=0.702, test=0.607) total time= 0.3s [CV 1/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.699, test=0.616) total time= 0.4s [CV 2/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.696, test=0.602) total time= 0.3s [CV 3/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.699, test=0.619) total time= 0.3s [CV 4/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.695, test=0.611) total time= 0.3s [CV 5/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.684, test=0.640) total time= 0.4s [CV 6/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.693, test=0.630) total time= 0.3s [CV 7/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.710, test=0.599) total time= 0.3s [CV 8/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.706, test=0.618) total time= 0.4s [CV 9/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.715, test=0.621) total time= 0.3s [CV 10/10] END ccp_alpha=0.00020050468346882386;, score=(train=0.696, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.695, test=0.622) total time= 0.3s [CV 2/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.695, test=0.602) total time= 0.4s [CV 3/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.699, test=0.618) total time= 0.3s [CV 4/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.695, test=0.613) total time= 0.3s [CV 5/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.684, test=0.640) total time= 0.3s [CV 6/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.693, test=0.630) total time= 0.3s [CV 7/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.708, test=0.599) total time= 0.3s [CV 8/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.706, test=0.618) total time= 0.3s [CV 9/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.715, test=0.621) total time= 0.4s [CV 10/10] END ccp_alpha=0.0002018976883164476;, score=(train=0.696, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.695, test=0.622) total time= 0.3s [CV 2/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.695, test=0.602) total time= 0.3s [CV 3/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.699, test=0.618) total time= 0.3s [CV 4/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.695, test=0.613) total time= 0.3s [CV 5/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.684, test=0.640) total time= 0.4s [CV 6/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.693, test=0.630) total time= 0.3s [CV 7/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.708, test=0.599) total time= 0.3s [CV 8/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.706, test=0.618) total time= 0.3s [CV 9/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.708, test=0.622) total time= 0.3s [CV 10/10] END ccp_alpha=0.00020204602793912166;, score=(train=0.696, test=0.606) total time= 0.4s [CV 1/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.695, test=0.622) total time= 0.3s [CV 2/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.693, test=0.601) total time= 0.4s [CV 3/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.699, test=0.618) total time= 0.3s [CV 4/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.695, test=0.613) total time= 0.3s [CV 5/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.684, test=0.640) total time= 0.3s [CV 6/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.693, test=0.630) total time= 0.3s [CV 7/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.708, test=0.599) total time= 0.3s [CV 8/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.704, test=0.620) total time= 0.3s [CV 9/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.708, test=0.622) total time= 0.4s [CV 10/10] END ccp_alpha=0.00020238997589921114;, score=(train=0.696, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.695, test=0.622) total time= 0.3s [CV 2/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.693, test=0.601) total time= 0.3s [CV 3/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.699, test=0.618) total time= 0.3s [CV 4/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.695, test=0.613) total time= 0.3s [CV 5/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.684, test=0.640) total time= 0.3s [CV 6/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.693, test=0.630) total time= 0.4s [CV 7/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.708, test=0.599) total time= 0.3s [CV 8/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.704, test=0.620) total time= 0.3s [CV 9/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.708, test=0.622) total time= 0.3s [CV 10/10] END ccp_alpha=0.0002025131657773932;, score=(train=0.696, test=0.606) total time= 0.3s [CV 1/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.686, test=0.626) total time= 0.3s [CV 2/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.684, test=0.608) total time= 0.3s [CV 3/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.691, test=0.621) total time= 0.4s [CV 4/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.688, test=0.617) total time= 0.3s [CV 5/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.677, test=0.646) total time= 0.3s [CV 6/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.684, test=0.631) total time= 0.3s [CV 7/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.694, test=0.600) total time= 0.3s [CV 8/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.689, test=0.628) total time= 0.3s [CV 9/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.691, test=0.628) total time= 0.3s [CV 10/10] END ccp_alpha=0.00021155201988468444;, score=(train=0.680, test=0.614) total time= 0.4s [CV 1/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.679, test=0.630) total time= 0.3s [CV 2/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.682, test=0.608) total time= 0.3s [CV 3/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.687, test=0.623) total time= 0.4s [CV 4/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.675, test=0.631) total time= 0.3s [CV 5/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.670, test=0.643) total time= 0.3s [CV 6/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.674, test=0.636) total time= 0.4s [CV 7/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.688, test=0.603) total time= 0.4s [CV 8/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.680, test=0.632) total time= 0.3s [CV 9/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.683, test=0.633) total time= 0.3s [CV 10/10] END ccp_alpha=0.0002181240063593012;, score=(train=0.680, test=0.613) total time= 0.3s [CV 1/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.678, test=0.629) total time= 0.3s [CV 2/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.675, test=0.607) total time= 0.3s [CV 3/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.682, test=0.625) total time= 0.4s [CV 4/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.669, test=0.636) total time= 0.3s [CV 5/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.669, test=0.643) total time= 0.3s [CV 6/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.674, test=0.636) total time= 0.3s [CV 7/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.688, test=0.603) total time= 0.3s [CV 8/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.677, test=0.636) total time= 0.3s [CV 9/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.680, test=0.630) total time= 0.3s [CV 10/10] END ccp_alpha=0.00022084080208352973;, score=(train=0.672, test=0.624) total time= 0.4s [CV 1/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.677, test=0.628) total time= 0.3s [CV 2/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.674, test=0.608) total time= 0.3s [CV 3/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.670, test=0.637) total time= 0.3s [CV 4/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.667, test=0.637) total time= 0.3s [CV 5/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.667, test=0.643) total time= 0.3s [CV 6/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.672, test=0.636) total time= 0.3s [CV 7/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.678, test=0.609) total time= 0.4s [CV 8/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.670, test=0.636) total time= 0.3s [CV 9/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.672, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.00022935988842869882;, score=(train=0.671, test=0.623) total time= 0.3s [CV 1/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.677, test=0.628) total time= 0.4s [CV 2/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.674, test=0.608) total time= 0.3s [CV 3/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.670, test=0.637) total time= 0.3s [CV 4/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.667, test=0.637) total time= 0.4s [CV 5/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.667, test=0.643) total time= 0.3s [CV 6/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.671, test=0.640) total time= 0.3s [CV 7/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.675, test=0.609) total time= 0.3s [CV 8/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.670, test=0.636) total time= 0.3s [CV 9/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.672, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.00022959707913577931;, score=(train=0.671, test=0.623) total time= 0.3s [CV 1/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.676, test=0.630) total time= 0.4s [CV 2/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.674, test=0.608) total time= 0.3s [CV 3/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.670, test=0.637) total time= 0.3s [CV 4/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.667, test=0.637) total time= 0.3s [CV 5/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.667, test=0.643) total time= 0.3s [CV 6/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.671, test=0.640) total time= 0.3s [CV 7/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.675, test=0.610) total time= 0.3s [CV 8/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.669, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.670, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.0002312025689174338;, score=(train=0.670, test=0.626) total time= 0.3s [CV 1/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.676, test=0.630) total time= 0.3s [CV 2/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.672, test=0.610) total time= 0.3s [CV 3/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.668, test=0.640) total time= 0.3s [CV 4/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.666, test=0.635) total time= 0.4s [CV 5/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.666, test=0.642) total time= 0.3s [CV 6/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.670, test=0.639) total time= 0.4s [CV 7/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.673, test=0.607) total time= 0.4s [CV 8/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.663, test=0.640) total time= 0.3s [CV 9/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.667, test=0.640) total time= 0.3s [CV 10/10] END ccp_alpha=0.00023564336632696942;, score=(train=0.670, test=0.626) total time= 0.3s [CV 1/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.673, test=0.635) total time= 0.4s [CV 2/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.672, test=0.610) total time= 0.3s [CV 3/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.668, test=0.640) total time= 0.3s [CV 4/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.666, test=0.635) total time= 0.3s [CV 5/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.666, test=0.642) total time= 0.3s [CV 6/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.670, test=0.639) total time= 0.3s [CV 7/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.673, test=0.607) total time= 0.3s [CV 8/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.663, test=0.640) total time= 0.4s [CV 9/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.667, test=0.640) total time= 0.3s [CV 10/10] END ccp_alpha=0.00023588861124008848;, score=(train=0.670, test=0.626) total time= 0.3s [CV 1/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.669, test=0.642) total time= 0.3s [CV 2/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.667, test=0.617) total time= 0.3s [CV 3/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.661, test=0.644) total time= 0.3s [CV 4/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.663, test=0.636) total time= 0.3s [CV 5/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.666, test=0.642) total time= 0.4s [CV 6/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.665, test=0.646) total time= 0.3s [CV 7/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.670, test=0.609) total time= 0.3s [CV 8/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.659, test=0.642) total time= 0.3s [CV 9/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.665, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.0002464699906998026;, score=(train=0.666, test=0.626) total time= 0.3s [CV 1/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.668, test=0.642) total time= 0.4s [CV 2/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.660, test=0.620) total time= 0.3s [CV 3/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.658, test=0.643) total time= 0.3s [CV 4/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.662, test=0.636) total time= 0.3s [CV 5/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.661, test=0.644) total time= 0.3s [CV 6/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.664, test=0.645) total time= 0.3s [CV 7/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.667, test=0.608) total time= 0.3s [CV 8/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.658, test=0.643) total time= 0.4s [CV 9/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.661, test=0.640) total time= 0.3s [CV 10/10] END ccp_alpha=0.00025399074491318577;, score=(train=0.665, test=0.626) total time= 0.3s [CV 1/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.661, test=0.642) total time= 0.3s [CV 2/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.657, test=0.622) total time= 0.3s [CV 3/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.657, test=0.641) total time= 0.3s [CV 4/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.660, test=0.637) total time= 0.3s [CV 5/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.657, test=0.647) total time= 0.4s [CV 6/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.662, test=0.646) total time= 0.3s [CV 7/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.667, test=0.608) total time= 0.3s [CV 8/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.656, test=0.641) total time= 0.3s [CV 9/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.658, test=0.642) total time= 0.3s [CV 10/10] END ccp_alpha=0.00026684758618800847;, score=(train=0.659, test=0.630) total time= 0.3s [CV 1/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.658, test=0.641) total time= 0.3s [CV 2/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.651, test=0.627) total time= 0.4s [CV 3/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.655, test=0.636) total time= 0.3s [CV 4/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.659, test=0.637) total time= 0.3s [CV 5/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.656, test=0.650) total time= 0.3s [CV 6/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.660, test=0.647) total time= 0.3s [CV 7/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.667, test=0.608) total time= 0.3s [CV 8/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.655, test=0.640) total time= 0.4s [CV 9/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.658, test=0.642) total time= 0.3s [CV 10/10] END ccp_alpha=0.0002791654362972393;, score=(train=0.656, test=0.632) total time= 0.3s [CV 1/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.656, test=0.640) total time= 0.3s [CV 2/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.651, test=0.627) total time= 0.3s [CV 3/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.654, test=0.636) total time= 0.3s [CV 4/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.657, test=0.643) total time= 0.3s [CV 5/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.655, test=0.650) total time= 0.3s [CV 6/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.660, test=0.647) total time= 0.3s [CV 7/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.664, test=0.612) total time= 0.3s [CV 8/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.654, test=0.642) total time= 0.3s [CV 9/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.654, test=0.641) total time= 0.3s [CV 10/10] END ccp_alpha=0.0002863267104832416;, score=(train=0.656, test=0.632) total time= 0.3s [CV 1/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.656, test=0.640) total time= 0.3s [CV 2/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.649, test=0.628) total time= 0.4s [CV 3/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.654, test=0.636) total time= 0.4s [CV 4/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.655, test=0.643) total time= 0.3s [CV 5/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.652, test=0.650) total time= 0.3s [CV 6/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.656, test=0.647) total time= 0.3s [CV 7/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.660, test=0.616) total time= 0.3s [CV 8/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.651, test=0.641) total time= 0.3s [CV 9/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.654, test=0.641) total time= 0.4s [CV 10/10] END ccp_alpha=0.00029437326144621245;, score=(train=0.656, test=0.631) total time= 0.3s [CV 1/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.656, test=0.640) total time= 0.3s [CV 2/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.649, test=0.628) total time= 0.4s [CV 3/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.654, test=0.636) total time= 0.3s [CV 4/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.655, test=0.643) total time= 0.3s [CV 5/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.652, test=0.650) total time= 0.3s [CV 6/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.656, test=0.647) total time= 0.4s [CV 7/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.660, test=0.616) total time= 0.3s [CV 8/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.651, test=0.641) total time= 0.3s [CV 9/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.654, test=0.641) total time= 0.3s [CV 10/10] END ccp_alpha=0.00029648756722890773;, score=(train=0.656, test=0.631) total time= 0.3s [CV 1/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.656, test=0.640) total time= 0.3s [CV 2/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.649, test=0.628) total time= 0.3s [CV 3/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.652, test=0.636) total time= 0.4s [CV 4/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.655, test=0.643) total time= 0.3s [CV 5/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.651, test=0.649) total time= 0.3s [CV 6/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.656, test=0.647) total time= 0.3s [CV 7/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.659, test=0.620) total time= 0.4s [CV 8/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.651, test=0.641) total time= 0.3s [CV 9/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.654, test=0.641) total time= 0.3s [CV 10/10] END ccp_alpha=0.0003009243945642226;, score=(train=0.655, test=0.631) total time= 0.4s [CV 1/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.654, test=0.637) total time= 0.3s [CV 2/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.647, test=0.625) total time= 0.3s [CV 3/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.652, test=0.636) total time= 0.3s [CV 4/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.653, test=0.644) total time= 0.3s [CV 5/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.651, test=0.649) total time= 0.3s [CV 6/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.654, test=0.647) total time= 0.4s [CV 7/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.659, test=0.620) total time= 0.3s [CV 8/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.650, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.654, test=0.641) total time= 0.4s [CV 10/10] END ccp_alpha=0.00032227196678651787;, score=(train=0.653, test=0.634) total time= 0.3s [CV 1/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.654, test=0.637) total time= 0.3s [CV 2/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.647, test=0.625) total time= 0.3s [CV 3/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.652, test=0.636) total time= 0.4s [CV 4/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.653, test=0.644) total time= 0.3s [CV 5/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.647, test=0.644) total time= 0.3s [CV 6/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.651, test=0.647) total time= 0.4s [CV 7/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.659, test=0.620) total time= 0.3s [CV 8/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.650, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.654, test=0.641) total time= 0.3s [CV 10/10] END ccp_alpha=0.00032470118384864827;, score=(train=0.653, test=0.634) total time= 0.4s [CV 1/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.651, test=0.635) total time= 0.3s [CV 2/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.644, test=0.621) total time= 0.3s [CV 3/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.650, test=0.638) total time= 0.3s [CV 4/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.651, test=0.642) total time= 0.3s [CV 5/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.647, test=0.644) total time= 0.3s [CV 6/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.646, test=0.644) total time= 0.3s [CV 7/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.654, test=0.617) total time= 0.4s [CV 8/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.648, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.650, test=0.644) total time= 0.3s [CV 10/10] END ccp_alpha=0.0003457201405152226;, score=(train=0.646, test=0.629) total time= 0.3s [CV 1/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.651, test=0.635) total time= 0.3s [CV 2/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.644, test=0.621) total time= 0.3s [CV 3/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.650, test=0.638) total time= 0.3s [CV 4/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.649, test=0.642) total time= 0.4s [CV 5/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.646, test=0.640) total time= 0.3s [CV 6/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.645, test=0.645) total time= 0.3s [CV 7/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.653, test=0.617) total time= 0.3s [CV 8/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.648, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.647, test=0.643) total time= 0.3s [CV 10/10] END ccp_alpha=0.0003505292911415159;, score=(train=0.646, test=0.629) total time= 0.3s [CV 1/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.646, test=0.632) total time= 0.4s [CV 2/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.644, test=0.621) total time= 0.3s [CV 3/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.650, test=0.638) total time= 0.3s [CV 4/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.649, test=0.642) total time= 0.4s [CV 5/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.646, test=0.640) total time= 0.3s [CV 6/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.640, test=0.641) total time= 0.3s [CV 7/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.648, test=0.615) total time= 0.3s [CV 8/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.648, test=0.638) total time= 0.4s [CV 9/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.643, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.00038591749843325945;, score=(train=0.643, test=0.628) total time= 0.3s [CV 1/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.646, test=0.632) total time= 0.3s [CV 2/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.644, test=0.621) total time= 0.3s [CV 3/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.650, test=0.638) total time= 0.3s [CV 4/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.648, test=0.641) total time= 0.3s [CV 5/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.646, test=0.640) total time= 0.4s [CV 6/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.640, test=0.640) total time= 0.3s [CV 7/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.648, test=0.615) total time= 0.3s [CV 8/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.648, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.643, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.0003881647886677178;, score=(train=0.643, test=0.628) total time= 0.3s [CV 1/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.646, test=0.632) total time= 0.3s [CV 2/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.644, test=0.621) total time= 0.4s [CV 3/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.647, test=0.642) total time= 0.3s [CV 4/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.647, test=0.640) total time= 0.3s [CV 5/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.646, test=0.640) total time= 0.3s [CV 6/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.640, test=0.640) total time= 0.3s [CV 7/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.648, test=0.615) total time= 0.3s [CV 8/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.648, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.641, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.00039607685695843345;, score=(train=0.643, test=0.628) total time= 0.3s [CV 1/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.646, test=0.632) total time= 0.3s [CV 2/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.644, test=0.621) total time= 0.3s [CV 3/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.647, test=0.642) total time= 0.3s [CV 4/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.647, test=0.640) total time= 0.3s [CV 5/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.646, test=0.640) total time= 0.4s [CV 6/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.640, test=0.640) total time= 0.4s [CV 7/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.648, test=0.615) total time= 0.3s [CV 8/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.648, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.641, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.00039651403944104824;, score=(train=0.643, test=0.628) total time= 0.3s [CV 1/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.630) total time= 0.3s [CV 2/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.619) total time= 0.4s [CV 3/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.645, test=0.640) total time= 0.3s [CV 4/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.642, test=0.639) total time= 0.3s [CV 5/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.639) total time= 0.3s [CV 6/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.636, test=0.643) total time= 0.3s [CV 7/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.645, test=0.614) total time= 0.3s [CV 8/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.645, test=0.638) total time= 0.3s [CV 9/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.641, test=0.639) total time= 0.4s [CV 10/10] END ccp_alpha=0.00045365442923022553;, score=(train=0.642, test=0.624) total time= 0.3s [CV 1/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.641, test=0.630) total time= 0.3s [CV 2/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.641, test=0.618) total time= 0.3s [CV 3/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.645, test=0.640) total time= 0.3s [CV 4/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.642, test=0.639) total time= 0.3s [CV 5/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.640, test=0.638) total time= 0.3s [CV 6/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.633, test=0.639) total time= 0.4s [CV 7/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.645, test=0.614) total time= 0.3s [CV 8/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.644, test=0.636) total time= 0.3s [CV 9/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.641, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.00046667525334502524;, score=(train=0.642, test=0.624) total time= 0.3s [CV 1/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.629) total time= 0.3s [CV 2/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.616) total time= 0.3s [CV 3/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.634, test=0.642) total time= 0.4s [CV 4/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.637) total time= 0.3s [CV 5/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.639, test=0.642) total time= 0.3s [CV 6/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.630, test=0.638) total time= 0.3s [CV 7/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.611) total time= 0.3s [CV 8/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.642, test=0.636) total time= 0.3s [CV 9/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.640, test=0.639) total time= 0.3s [CV 10/10] END ccp_alpha=0.0005381005070070333;, score=(train=0.638, test=0.625) total time= 0.3s [CV 1/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.629) total time= 0.3s [CV 2/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.616) total time= 0.3s [CV 3/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.634, test=0.641) total time= 0.4s [CV 4/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.637) total time= 0.3s [CV 5/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.639, test=0.642) total time= 0.3s [CV 6/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.630, test=0.637) total time= 0.3s [CV 7/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.611) total time= 0.4s [CV 8/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.638, test=0.637) total time= 0.3s [CV 9/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.635, test=0.636) total time= 0.3s [CV 10/10] END ccp_alpha=0.0005914741008202773;, score=(train=0.634, test=0.620) total time= 0.3s [CV 1/10] END ccp_alpha=0.000688561405553012;, score=(train=0.628, test=0.619) total time= 0.3s [CV 2/10] END ccp_alpha=0.000688561405553012;, score=(train=0.636, test=0.612) total time= 0.3s [CV 3/10] END ccp_alpha=0.000688561405553012;, score=(train=0.634, test=0.641) total time= 0.3s [CV 4/10] END ccp_alpha=0.000688561405553012;, score=(train=0.637, test=0.638) total time= 0.4s [CV 5/10] END ccp_alpha=0.000688561405553012;, score=(train=0.636, test=0.642) total time= 0.3s [CV 6/10] END ccp_alpha=0.000688561405553012;, score=(train=0.630, test=0.637) total time= 0.3s [CV 7/10] END ccp_alpha=0.000688561405553012;, score=(train=0.637, test=0.613) total time= 0.3s [CV 8/10] END ccp_alpha=0.000688561405553012;, score=(train=0.635, test=0.631) total time= 0.3s [CV 9/10] END ccp_alpha=0.000688561405553012;, score=(train=0.635, test=0.636) total time= 0.3s [CV 10/10] END ccp_alpha=0.000688561405553012;, score=(train=0.633, test=0.623) total time= 0.3s [CV 1/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.626, test=0.616) total time= 0.4s [CV 2/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.632, test=0.610) total time= 0.3s [CV 3/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.630, test=0.639) total time= 0.3s [CV 4/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.626, test=0.629) total time= 0.3s [CV 5/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.629, test=0.633) total time= 0.3s [CV 6/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.625, test=0.636) total time= 0.3s [CV 7/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.633, test=0.610) total time= 0.3s [CV 8/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.632, test=0.629) total time= 0.4s [CV 9/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.631, test=0.633) total time= 0.3s [CV 10/10] END ccp_alpha=0.0007462466982758043;, score=(train=0.632, test=0.624) total time= 0.3s [CV 1/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.625, test=0.615) total time= 0.3s [CV 2/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.616, test=0.595) total time= 0.3s [CV 3/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.613, test=0.622) total time= 0.3s [CV 4/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.617, test=0.616) total time= 0.3s [CV 5/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.615, test=0.616) total time= 0.4s [CV 6/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.624, test=0.634) total time= 0.3s [CV 7/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.625, test=0.610) total time= 0.3s [CV 8/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.614, test=0.610) total time= 0.3s [CV 9/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.626, test=0.630) total time= 0.3s [CV 10/10] END ccp_alpha=0.0012949764720767767;, score=(train=0.627, test=0.622) total time= 0.3s [CV 1/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.625, test=0.615) total time= 0.3s [CV 2/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.616, test=0.595) total time= 0.4s [CV 3/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.613, test=0.622) total time= 0.3s [CV 4/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.612, test=0.609) total time= 0.3s [CV 5/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.615, test=0.616) total time= 0.3s [CV 6/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.624, test=0.634) total time= 0.3s [CV 7/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.625, test=0.610) total time= 0.3s [CV 8/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.614, test=0.610) total time= 0.3s [CV 9/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.626, test=0.630) total time= 0.4s [CV 10/10] END ccp_alpha=0.0013327348036736852;, score=(train=0.627, test=0.622) total time= 0.3s [CV 1/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.614, test=0.614) total time= 0.3s [CV 2/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.603, test=0.577) total time= 0.3s [CV 3/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.601, test=0.599) total time= 0.3s [CV 4/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.612, test=0.609) total time= 0.3s [CV 5/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.615, test=0.616) total time= 0.3s [CV 6/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.601, test=0.614) total time= 0.4s [CV 7/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.615, test=0.604) total time= 0.3s [CV 8/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.614, test=0.610) total time= 0.3s [CV 9/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.615, test=0.619) total time= 0.3s [CV 10/10] END ccp_alpha=0.0026813285638898066;, score=(train=0.602, test=0.603) total time= 0.3s [CV 1/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.600, test=0.606) total time= 0.3s [CV 2/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.597, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.595, test=0.593) total time= 0.4s [CV 4/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.595, test=0.593) total time= 0.3s [CV 5/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.604) total time= 0.3s [CV 6/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.601, test=0.614) total time= 0.3s [CV 7/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.593) total time= 0.3s [CV 8/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.601, test=0.599) total time= 0.3s [CV 9/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.610) total time= 0.3s [CV 10/10] END ccp_alpha=0.0032035682748021987;, score=(train=0.602, test=0.603) total time= 0.4s [CV 1/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time= 0.3s [CV 2/10] END ccp_alpha=0.019928402011802793;, score=(train=0.597, test=0.571) total time= 0.3s [CV 3/10] END ccp_alpha=0.019928402011802793;, score=(train=0.595, test=0.593) total time= 0.3s [CV 4/10] END ccp_alpha=0.019928402011802793;, score=(train=0.595, test=0.593) total time= 0.3s [CV 5/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time= 0.3s [CV 6/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time= 0.3s [CV 7/10] END ccp_alpha=0.019928402011802793;, score=(train=0.595, test=0.589) total time= 0.4s [CV 8/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time= 0.3s [CV 9/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time= 0.3s [CV 10/10] END ccp_alpha=0.019928402011802793;, score=(train=0.500, test=0.500) total time= 0.3s
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=DecisionTreeClassifier(random_state=42),
param_grid={'ccp_alpha': array([0.00000000e+00, 2.74285714e-05, 3.17460317e-05, ...,
2.68132856e-03, 3.20356827e-03, 1.99284020e-02])},
return_train_score=True, scoring='roc_auc', verbose=4)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=DecisionTreeClassifier(random_state=42),
param_grid={'ccp_alpha': array([0.00000000e+00, 2.74285714e-05, 3.17460317e-05, ...,
2.68132856e-03, 3.20356827e-03, 1.99284020e-02])},
return_train_score=True, scoring='roc_auc', verbose=4)DecisionTreeClassifier(ccp_alpha=0.0003009243945642226, random_state=42)
DecisionTreeClassifier(ccp_alpha=0.0003009243945642226, random_state=42)
report_GridSearchCV_results(grid_search_post_prune)
- Best combination of hyperparameters:
{'ccp_alpha': 0.0003009243945642226}
- Best mean_test_score:
0.6375618358423202
- Score by fold for best estimator:
[0.6397542997542998, 0.6280936936936937, 0.6358545454545455, 0.6431108927108927, 0.649273054873055, 0.6468022932022932, 0.6197610400092239, 0.6412566952821189, 0.6407404641363479, 0.6309713793067303]
- Top 10 hyperparameter combinations by mean_test_score:
| mean_test_score | param_ccp_alpha | |
|---|---|---|
| rank_test_score | ||
| 1 | 0.637562 | 0.000301 |
| 2 | 0.637359 | 0.000294 |
| 2 | 0.637359 | 0.000296 |
| 4 | 0.637018 | 0.000286 |
| 5 | 0.636972 | 0.000322 |
| 6 | 0.636471 | 0.000325 |
| 7 | 0.636019 | 0.000279 |
| 8 | 0.635712 | 0.000267 |
| 9 | 0.635158 | 0.000346 |
| 10 | 0.634747 | 0.000254 |
compare_performance(grid_search_post_prune)
| train_AUC | val_AUC | |
|---|---|---|
| 1 | 1.0 | 0.537137 |
| 2 | 1.0 | 0.537137 |
| 3 | 1.0 | 0.537111 |
| 4 | 1.0 | 0.537111 |
| 5 | 1.0 | 0.537111 |
| 6 | 1.0 | 0.537139 |
| 7 | 1.0 | 0.537139 |
| 8 | 1.0 | 0.537139 |
| 9 | 1.0 | 0.537139 |
| 10 | 1.0 | 0.537139 |
| Mean | 1.0 | 0.537131 |
best_model_post_prune=grid_search_post_prune.best_estimator_
plot_feature_importance_chart(best_model_post_prune, X_train, y_train, cv, "Post-pruned Classification Tree")
# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_post_prune, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()
evaluate_model(best_model_post_prune, X_test, y_test)
Test AUC: 0.64 Accuracy: 0.61 Confusion Matrix: [[2627 1373] [1546 1954]]
Classification Report:
precision recall f1-score support
0 0.63 0.66 0.64 4000
1 0.59 0.56 0.57 3500
accuracy 0.61 7500
macro avg 0.61 0.61 0.61 7500
weighted avg 0.61 0.61 0.61 7500
plot_roc_curve(best_model_post_prune, X_test, y_test)
Random Forest¶
# Initialize model
randomforest = RandomForestClassifier(max_depth = 6, random_state = 42, bootstrap=True)
# Define the hyperparameter grid
rf_param_grid = {
'max_depth': [2, 3, 4],
'min_samples_leaf': [500, 1000, 2000],
'max_features': [2, 3],
}
# Create a GridSearchCV object
grid_search_rf = GridSearchCV(estimator=randomforest, param_grid=rf_param_grid, cv=cv, scoring='roc_auc', verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
grid_search_rf.fit(X_train, y_train)
Fitting 10 folds for each of 18 candidates, totalling 180 fits [CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.634) total time= 0.5s [CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.618) total time= 0.5s [CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.642) total time= 0.6s [CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.631) total time= 0.6s [CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.644) total time= 0.6s [CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.638, test=0.665) total time= 0.6s [CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.610) total time= 0.5s [CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.631) total time= 0.5s [CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.642) total time= 0.6s [CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.629) total time= 0.5s [CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.632, test=0.626) total time= 0.3s [CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.633, test=0.604) total time= 0.5s [CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.633, test=0.636) total time= 0.5s [CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.631, test=0.627) total time= 0.4s [CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.641) total time= 0.4s [CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.631, test=0.663) total time= 0.6s [CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.632, test=0.599) total time= 0.6s [CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.631, test=0.629) total time= 0.5s [CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.630, test=0.632) total time= 0.5s [CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.632, test=0.623) total time= 0.5s [CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.605, test=0.602) total time= 0.5s [CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.574) total time= 0.5s [CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.602, test=0.607) total time= 0.4s [CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.598) total time= 0.4s [CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.619) total time= 0.4s [CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.601, test=0.630) total time= 0.5s [CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.573) total time= 0.5s [CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.611) total time= 0.5s [CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.597) total time= 0.4s [CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.586) total time= 0.6s [CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.640, test=0.636) total time= 0.5s [CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.618) total time= 0.7s [CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.645) total time= 0.8s [CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.640, test=0.630) total time= 0.7s [CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.640, test=0.646) total time= 0.7s [CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.638, test=0.662) total time= 0.7s [CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.612) total time= 0.6s [CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.636) total time= 0.5s [CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.639, test=0.649) total time= 0.5s [CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.633) total time= 0.4s [CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.635, test=0.627) total time= 0.6s [CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.613) total time= 0.5s [CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.643) total time= 0.4s [CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.636, test=0.631) total time= 0.4s [CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.634, test=0.643) total time= 0.5s [CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.632, test=0.659) total time= 0.5s [CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.640, test=0.604) total time= 0.4s [CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.634) total time= 0.5s [CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.634, test=0.642) total time= 0.5s [CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.634, test=0.623) total time= 0.4s [CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.612) total time= 0.4s [CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.597) total time= 0.4s [CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.618, test=0.620) total time= 0.5s [CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.612) total time= 0.5s [CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.628) total time= 0.4s [CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.634) total time= 0.4s [CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.582) total time= 0.4s [CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.614) total time= 0.4s [CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.616, test=0.617) total time= 0.4s [CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time= 0.5s [CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.638) total time= 0.5s [CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.621) total time= 0.4s [CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.644) total time= 0.5s [CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.636) total time= 0.5s [CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.649) total time= 0.4s [CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.666) total time= 0.6s [CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.613) total time= 0.6s [CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.637) total time= 0.6s [CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.648) total time= 0.5s [CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.636) total time= 0.4s [CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.633) total time= 0.4s [CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.611) total time= 0.4s [CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.638) total time= 0.4s [CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.633) total time= 0.4s [CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.647) total time= 0.4s [CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.664) total time= 0.4s [CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.605) total time= 0.4s [CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.633) total time= 0.4s [CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.639) total time= 0.4s [CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.629) total time= 0.4s [CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.605, test=0.602) total time= 0.4s [CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.575) total time= 0.5s [CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.602, test=0.607) total time= 0.4s [CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.598) total time= 0.6s [CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.618) total time= 0.5s [CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.601, test=0.630) total time= 0.5s [CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.572) total time= 0.3s [CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.611) total time= 0.4s [CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.597) total time= 0.5s [CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.586) total time= 0.4s [CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.637) total time= 0.5s [CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.619) total time= 0.5s [CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.650) total time= 0.6s [CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.643, test=0.633) total time= 0.5s [CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.648) total time= 0.6s [CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.641, test=0.663) total time= 0.5s [CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.615) total time= 0.5s [CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.639) total time= 0.5s [CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.643, test=0.649) total time= 0.5s [CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.634) total time= 0.5s [CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.638, test=0.630) total time= 0.5s [CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.613) total time= 0.6s [CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.643) total time= 0.9s [CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.638, test=0.631) total time= 0.8s [CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.636, test=0.645) total time= 0.7s [CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.633, test=0.659) total time= 0.6s [CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.605) total time= 0.6s [CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.636) total time= 0.7s [CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.642) total time= 0.6s [CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.637, test=0.625) total time= 0.6s [CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.616, test=0.612) total time= 0.5s [CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.596) total time= 0.5s [CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.620) total time= 0.5s [CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.611) total time= 0.6s [CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.626) total time= 0.7s [CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.634) total time= 0.7s [CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.582) total time= 0.5s [CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.614) total time= 0.7s [CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.615, test=0.616) total time= 0.5s [CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time= 0.6s [CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.636) total time= 0.6s [CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.621) total time= 0.5s [CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.644) total time= 0.6s [CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.637) total time= 0.6s [CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.648) total time= 0.5s [CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.669) total time= 0.5s [CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.613) total time= 0.5s [CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.638) total time= 0.4s [CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.646) total time= 0.4s [CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.635) total time= 0.5s [CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.633) total time= 0.6s [CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.636, test=0.609) total time= 0.5s [CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.636) total time= 0.6s [CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.631) total time= 0.5s [CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.649) total time= 0.6s [CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.664) total time= 0.5s [CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.602) total time= 0.6s [CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.632) total time= 0.6s [CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.634, test=0.637) total time= 0.6s [CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.635, test=0.627) total time= 0.5s [CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.605, test=0.602) total time= 0.5s [CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.575) total time= 0.4s [CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.602, test=0.607) total time= 0.5s [CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.598) total time= 0.8s [CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.618) total time= 0.8s [CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.601, test=0.630) total time= 0.8s [CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.572) total time= 0.9s [CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.611) total time= 0.7s [CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.604, test=0.597) total time= 0.7s [CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.603, test=0.586) total time= 0.6s [CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.636) total time= 0.7s [CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.623) total time= 0.6s [CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.650) total time= 0.7s [CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.633) total time= 0.8s [CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.648) total time= 0.7s [CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.642, test=0.664) total time= 0.7s [CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.615) total time= 0.6s [CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.641) total time= 0.6s [CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.643, test=0.649) total time= 0.7s [CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.634) total time= 0.6s [CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.631) total time= 0.6s [CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.614) total time= 0.6s [CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.645) total time= 0.7s [CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.633) total time= 0.7s [CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.647) total time= 0.5s [CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.636, test=0.661) total time= 0.6s [CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.607) total time= 0.6s [CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.640, test=0.638) total time= 0.6s [CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.638, test=0.644) total time= 0.6s [CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.639, test=0.628) total time= 0.6s [CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.616, test=0.612) total time= 0.6s [CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.596) total time= 0.5s [CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.620) total time= 0.5s [CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.611) total time= 0.9s [CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.626) total time= 1.2s [CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.610, test=0.634) total time= 1.2s [CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.620, test=0.582) total time= 1.0s [CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.611, test=0.614) total time= 1.3s [CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.615, test=0.616) total time= 1.0s [CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.617, test=0.605) total time= 0.6s
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=RandomForestClassifier(max_depth=6, random_state=42),
param_grid={'max_depth': [2, 3, 4], 'max_features': [2, 3],
'min_samples_leaf': [500, 1000, 2000]},
return_train_score=True, scoring='roc_auc', verbose=4)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=RandomForestClassifier(max_depth=6, random_state=42),
param_grid={'max_depth': [2, 3, 4], 'max_features': [2, 3],
'min_samples_leaf': [500, 1000, 2000]},
return_train_score=True, scoring='roc_auc', verbose=4)RandomForestClassifier(max_depth=4, max_features=3, min_samples_leaf=500,
random_state=42)RandomForestClassifier(max_depth=4, max_features=3, min_samples_leaf=500,
random_state=42)report_GridSearchCV_results(grid_search_rf)
- Best combination of hyperparameters:
{'max_depth': 4, 'max_features': 3, 'min_samples_leaf': 500}
- Best mean_test_score:
0.6392741898648436
- Score by fold for best estimator:
[0.6364894348894349, 0.6227996723996724, 0.6501444717444718, 0.6332481572481573, 0.6477654381654381, 0.6635210483210483, 0.6147461295766381, 0.6406114063499052, 0.6493650618953283, 0.634051078058342]
- Top 10 hyperparameter combinations by mean_test_score:
| mean_test_score | param_max_depth | param_min_samples_leaf | param_max_features | |
|---|---|---|---|---|
| rank_test_score | ||||
| 1 | 0.639274 | 4 | 500 | 3 |
| 2 | 0.638760 | 3 | 500 | 2 |
| 3 | 0.638721 | 3 | 500 | 3 |
| 4 | 0.638607 | 4 | 500 | 2 |
| 5 | 0.636623 | 2 | 500 | 3 |
| 6 | 0.634800 | 4 | 1000 | 3 |
| 7 | 0.634666 | 2 | 500 | 2 |
| 8 | 0.633292 | 3 | 1000 | 2 |
| 9 | 0.632992 | 3 | 1000 | 3 |
| 10 | 0.631938 | 2 | 1000 | 3 |
compare_performance(grid_search_rf)
| train_AUC | val_AUC | |
|---|---|---|
| 1 | 0.639827 | 0.634666 |
| 2 | 0.632097 | 0.627992 |
| 3 | 0.603159 | 0.599672 |
| 4 | 0.641148 | 0.636623 |
| 5 | 0.635874 | 0.631938 |
| 6 | 0.615571 | 0.612199 |
| 7 | 0.644051 | 0.638760 |
| 8 | 0.637475 | 0.633292 |
| 9 | 0.603204 | 0.599673 |
| 10 | 0.643913 | 0.638721 |
| Mean | 0.629632 | 0.625353 |
best_model_rf=grid_search_rf.best_estimator_
plot_feature_importance_chart(best_model_rf, X_train, y_train, cv, "Random Forest")
evaluate_model(best_model_rf, X_test, y_test)
Test AUC: 0.64 Accuracy: 0.60 Confusion Matrix: [[3438 562] [2441 1059]]
Classification Report:
precision recall f1-score support
0 0.58 0.86 0.70 4000
1 0.65 0.30 0.41 3500
accuracy 0.60 7500
macro avg 0.62 0.58 0.55 7500
weighted avg 0.62 0.60 0.56 7500
plot_roc_curve(best_model_rf, X_test, y_test)
XGBoost¶
# Initialize model
xgb_model = xgb.XGBClassifier(random_state = 42)
# Define the hyperparameter grid
xgb_param_grid = {
'colsample_bytree': [0.3, 0.7],
'n_estimators': [50, 100, 200],
'max_depth': [2, 5, 10],
'alpha': [0, 0.1, 1], # Alpha/lasso regularisation
'lambda': [0, 0.1, 1], # Lambda/ridge regularisation
'learning_rate': [0.01, 0.05]
}
# Create a GridSearchCV object
grid_search_xgb = GridSearchCV(param_grid=xgb_param_grid, estimator=xgb_model,
scoring='roc_auc', cv=cv, verbose=4, return_train_score=True)
# Fit the GridSearchCV object to the training data
grid_search_xgb.fit(X_train, y_train)
Fitting 10 folds for each of 324 candidates, totalling 3240 fits [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time= 0.0s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.647) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.6s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.669) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time= 0.6s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.8s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time= 0.8s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.660) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.672) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.629) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.651) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.653) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.660) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.662) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.673) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.689, test=0.631) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.652) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time= 0.7s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.641) total time= 0.8s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.662) total time= 0.9s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time= 0.8s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.661) total time= 0.7s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.675) total time= 1.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.697, test=0.632) total time= 1.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.652) total time= 1.0s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time= 1.0s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time= 1.0s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.647) total time= 0.7s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.635) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.658) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.647) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.654) total time= 0.8s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.668) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.834, test=0.628) total time= 0.7s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.646) total time= 0.7s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.837, test=0.652) total time= 0.8s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.645) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.648) total time= 1.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.639) total time= 0.7s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.659) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.651) total time= 0.8s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.660) total time= 0.8s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.670) total time= 0.8s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.842, test=0.631) total time= 1.6s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.645) total time= 0.8s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.660) total time= 0.7s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.647) total time= 1.0s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.646) total time= 1.7s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.637) total time= 1.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.659) total time= 1.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.654) total time= 1.5s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.657) total time= 1.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.669) total time= 1.4s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.631) total time= 1.6s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.646) total time= 1.5s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.872, test=0.659) total time= 1.9s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.650) total time= 1.9s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.646) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time= 0.6s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.653) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.644) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.664) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.652) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.660) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.676) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.635) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.665) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.650) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.655) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.645) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.664) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.652) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.678) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.637) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.652) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.669) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.651) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.655) total time= 0.6s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.648) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.662) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.655) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.662) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.679) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.640) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.653) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.667) total time= 0.6s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.653) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.645) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.638) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.656) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.874, test=0.639) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.649) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.672) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.629) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.642) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.645) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.640) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.640) total time= 0.6s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.637) total time= 0.7s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.654) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.639) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.648) total time= 0.7s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.673) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.629) total time= 0.6s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.638) total time= 0.7s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.650) total time= 0.6s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.638) total time= 0.7s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.629) total time= 1.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.630) total time= 1.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.650) total time= 1.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.640) total time= 1.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.646) total time= 1.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.663) total time= 1.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.628) total time= 1.2s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.631) total time= 1.4s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.652) total time= 1.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.637) total time= 1.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.648) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.670) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.672) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.628) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.653) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.652) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.660) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.662) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.673) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.689, test=0.631) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.641) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.662) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.676) total time= 0.5s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.632) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.653) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.655) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.648) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.637) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.656) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.647) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.654) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.667) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.629) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.647) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.835, test=0.654) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.645) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.649) total time= 0.7s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.639) total time= 0.7s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.659) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.650) total time= 0.7s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.660) total time= 0.7s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.670) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.632) total time= 0.7s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.645) total time= 0.7s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.661) total time= 0.7s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.647) total time= 0.7s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.646) total time= 1.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.637) total time= 1.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.658) total time= 1.3s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.653) total time= 1.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.657) total time= 1.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.669) total time= 1.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.632) total time= 1.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.647) total time= 1.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.660) total time= 1.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.650) total time= 1.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.646) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.652) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.643) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.663) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.661) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.676) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.635) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.665) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.656) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.645) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.664) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.654) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.678) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.636) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.652) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.669) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.653) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.658) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.645) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.664) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.654) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.662) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.677) total time= 0.6s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.638) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.652) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.668) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.655) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.644) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.634) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.655) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.638) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.651) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.672) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.628) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.640) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.648) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.641) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.640) total time= 0.6s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.633) total time= 0.7s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.654) total time= 0.6s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.636) total time= 0.7s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.652) total time= 0.6s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.671) total time= 0.6s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.629) total time= 0.7s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.636) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.653) total time= 0.6s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.638) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.632) total time= 1.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.630) total time= 1.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.654) total time= 1.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.636) total time= 1.2s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.652) total time= 1.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.662) total time= 1.5s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.626) total time= 1.5s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.630) total time= 1.4s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.648) total time= 1.4s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.636) total time= 1.7s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.628) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.629) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.652) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.669) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.651) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.640) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.672) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.629) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.652) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.640) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.660) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.651) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.662) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.673) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.632) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.664) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time= 0.7s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.641) total time= 0.9s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.661) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.654) total time= 1.0s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.661) total time= 1.0s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.675) total time= 1.0s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.632) total time= 0.9s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time= 1.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.665) total time= 0.6s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time= 0.8s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.651) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.637) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.657) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.649) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.655) total time= 0.6s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.671) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.631) total time= 0.6s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.645) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.656) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.648) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.651) total time= 1.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.640) total time= 0.8s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.659) total time= 0.9s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.651) total time= 0.9s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.661) total time= 0.8s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.671) total time= 1.1s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.633) total time= 1.0s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.646) total time= 1.0s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.662) total time= 1.1s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.651) total time= 0.9s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.648) total time= 1.9s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.636) total time= 2.7s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.661) total time= 2.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.654) total time= 2.3s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.659) total time= 2.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.670) total time= 2.8s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.633) total time= 1.7s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.649) total time= 1.5s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.661) total time= 1.7s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.655) total time= 2.5s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.642) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.628) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.649) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time= 0.6s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.636) total time= 1.1s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.655) total time= 0.5s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.661) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time= 0.5s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.633) total time= 0.6s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.654) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.643) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.663) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.655) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.660) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.675) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.633) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.651) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.663) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.651) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.657) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.645) total time= 0.8s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.654) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time= 0.8s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.677) total time= 0.5s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.635) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time= 0.7s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.665) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.653) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.658) total time= 0.9s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.647) total time= 1.0s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.664) total time= 1.0s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.656) total time= 0.8s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.664) total time= 0.9s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.676) total time= 1.0s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.637) total time= 1.0s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.651) total time= 1.0s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.666) total time= 0.8s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.658) total time= 1.0s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.652) total time= 0.6s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.635) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.655) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.645) total time= 0.5s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.659) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.669) total time= 0.6s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.629) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.644) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.651) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.643) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.646) total time= 0.8s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.633) total time= 0.8s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.654) total time= 0.8s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.645) total time= 0.8s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.657) total time= 0.7s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.669) total time= 0.8s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.631) total time= 0.9s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.641) total time= 0.9s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.654) total time= 0.8s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.643) total time= 0.8s [CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.639) total time= 1.3s [CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.630) total time= 1.3s [CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.649) total time= 1.6s [CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.646) total time= 1.6s [CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.652) total time= 2.3s [CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.660) total time= 1.9s [CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.630) total time= 1.6s [CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.637) total time= 1.7s [CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.652) total time= 1.4s [CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.642) total time= 1.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.637) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.5s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.7s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time= 0.6s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.642) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.651) total time= 0.7s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.646) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.655) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.648) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.641) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.648) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.637) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.642) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.650) total time= 0.7s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.640) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.656) total time= 0.6s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.652) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.656) total time= 0.6s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.671) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.630) total time= 0.7s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.648) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time= 0.7s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.643) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.633) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.636) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.649) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.639) total time= 0.5s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.652) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.660) total time= 0.5s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.620) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.640) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.647) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.641) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.636) total time= 0.9s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.633) total time= 1.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.651) total time= 0.9s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.643) total time= 0.9s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.654) total time= 1.0s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.664) total time= 1.1s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.621) total time= 1.0s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.637) total time= 1.0s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.852, test=0.651) total time= 0.9s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.643) total time= 1.0s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.635) total time= 1.9s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.877, test=0.639) total time= 1.9s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.873, test=0.650) total time= 1.9s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.641) total time= 1.9s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.651) total time= 1.7s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.871, test=0.666) total time= 1.9s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.624) total time= 1.8s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.874, test=0.636) total time= 1.7s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.877, test=0.650) total time= 1.9s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.641) total time= 1.8s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.665) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.643) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.630) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.651) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.668) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.635) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time= 0.5s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.649) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.652) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.669) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.647) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.660) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.645) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.653) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.643) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.657) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.650) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.658) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.672) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.716, test=0.631) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.650) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.665) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.648) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.654) total time= 0.6s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.645) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.657) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.654) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.658) total time= 0.6s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.675) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.751, test=0.635) total time= 1.0s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.646) total time= 0.8s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.665) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.649) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.881, test=0.630) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.884, test=0.631) total time= 0.8s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.889, test=0.639) total time= 0.6s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.877, test=0.636) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.881, test=0.643) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.654) total time= 0.9s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.886, test=0.614) total time= 0.7s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.629) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.886, test=0.647) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.637) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.628) total time= 0.9s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.633) total time= 0.8s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.931, test=0.638) total time= 0.8s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.926, test=0.635) total time= 0.8s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.639) total time= 0.8s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.926, test=0.653) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.615) total time= 0.8s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.938, test=0.624) total time= 0.7s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.645) total time= 0.7s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.639) total time= 0.7s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.617) total time= 1.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.976, test=0.629) total time= 2.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.637) total time= 1.3s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.633) total time= 1.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.640) total time= 1.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.976, test=0.645) total time= 1.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.612) total time= 1.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.617) total time= 1.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.642) total time= 1.3s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.636) total time= 1.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.637) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.642) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.651) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.645) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.632) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.654) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.648) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.642) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.636) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time= 0.4s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.642) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.650) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.640) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.655) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.651) total time= 0.7s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.657) total time= 0.7s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.670) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.629) total time= 0.7s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.648) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time= 0.7s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.644) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.633) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.633) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.648) total time= 0.6s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.826, test=0.640) total time= 0.5s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.652) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.661) total time= 0.5s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.620) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.644) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.649) total time= 1.1s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.642) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.635) total time= 0.8s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.632) total time= 0.9s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.650) total time= 1.1s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.642) total time= 0.9s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.653) total time= 0.9s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.663) total time= 0.9s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.847, test=0.621) total time= 0.9s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.641) total time= 0.8s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.851, test=0.652) total time= 0.9s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.643) total time= 0.9s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.635) total time= 1.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.640) total time= 1.5s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.873, test=0.650) total time= 1.4s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.641) total time= 1.5s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.651) total time= 1.5s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.665) total time= 1.5s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.623) total time= 1.9s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.871, test=0.637) total time= 1.8s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.652) total time= 1.9s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.641) total time= 1.9s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.665) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.645) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.630) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.669) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.636) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.632) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.652) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.650) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.658) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.652) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.668) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.649) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.644) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.652) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.642) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.659) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.648) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.658) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.671) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.716, test=0.631) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.650) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.665) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.649) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.650) total time= 0.7s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.646) total time= 0.5s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.660) total time= 0.6s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.652) total time= 0.5s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.658) total time= 0.6s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.672) total time= 0.5s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.637) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.649) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.664) total time= 0.6s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.651) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.628) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.883, test=0.634) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.883, test=0.646) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.636) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.649) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.651) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.884, test=0.615) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.632) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.884, test=0.652) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.637) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.623) total time= 0.8s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.634) total time= 0.8s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.923, test=0.641) total time= 0.7s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.636) total time= 0.8s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.922, test=0.641) total time= 0.7s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.931, test=0.648) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.931, test=0.619) total time= 0.7s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.628) total time= 0.7s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.933, test=0.650) total time= 0.8s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.920, test=0.636) total time= 0.8s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.614) total time= 1.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.967, test=0.631) total time= 1.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.640) total time= 1.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.634) total time= 1.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.637) total time= 1.0s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.974, test=0.641) total time= 1.1s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.620) total time= 1.3s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.618) total time= 1.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.644) total time= 1.3s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.634) total time= 1.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.648) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.643) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.650) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time= 0.4s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.646) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.632) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.650) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.664) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.648) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.643) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.647) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.667) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.656) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.643) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time= 0.5s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.651) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.655) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.669) total time= 0.6s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.629) total time= 0.6s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.648) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time= 0.7s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.641) total time= 0.4s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.636) total time= 0.4s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.649) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.641) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.653) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.660) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.621) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.644) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.650) total time= 0.4s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.646) total time= 0.5s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.640) total time= 0.8s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.638) total time= 0.9s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.649) total time= 1.0s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.645) total time= 0.9s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.654) total time= 1.1s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.664) total time= 0.9s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.623) total time= 1.0s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.642) total time= 1.0s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.656) total time= 0.9s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.645) total time= 0.9s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.640) total time= 2.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.644) total time= 1.8s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.647) total time= 1.8s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.644) total time= 1.6s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.652) total time= 2.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.666) total time= 1.9s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.622) total time= 2.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.641) total time= 1.6s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.656) total time= 1.7s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.642) total time= 1.7s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time= 0.1s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.631) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.649) total time= 0.1s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.1s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.664) total time= 0.1s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.643) total time= 0.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.629) total time= 0.1s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.655) total time= 0.1s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.650) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.669) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time= 0.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time= 0.2s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.643) total time= 0.2s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.653) total time= 0.3s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.630) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.649) total time= 0.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.638) total time= 0.2s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time= 0.2s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.652) total time= 0.2s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.657) total time= 0.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.667) total time= 0.2s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.629) total time= 0.1s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.647) total time= 0.2s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time= 0.1s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.643) total time= 0.1s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.653) total time= 0.3s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.641) total time= 0.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.659) total time= 0.3s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.650) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.660) total time= 0.3s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.672) total time= 0.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.630) total time= 0.3s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.649) total time= 0.4s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.664) total time= 0.3s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.648) total time= 0.3s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.653) total time= 0.8s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.645) total time= 0.6s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.661) total time= 0.5s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.655) total time= 0.6s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.660) total time= 0.6s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.672) total time= 0.7s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.636) total time= 0.6s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.647) total time= 0.6s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.667) total time= 0.6s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.650) total time= 0.7s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.637) total time= 0.7s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.639) total time= 0.7s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.648) total time= 0.4s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.637) total time= 0.4s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.654) total time= 0.5s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.655) total time= 0.4s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.617) total time= 0.5s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.642) total time= 0.5s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.651) total time= 0.5s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.639) total time= 0.6s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.634) total time= 0.8s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.641) total time= 1.3s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.648) total time= 1.3s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.644) total time= 1.8s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.650) total time= 1.2s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.658) total time= 1.5s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.620) total time= 1.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.637) total time= 1.1s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.654) total time= 0.8s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.900, test=0.638) total time= 0.7s [CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.950, test=0.622) total time= 1.2s [CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.948, test=0.638) total time= 1.4s [CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.645) total time= 2.8s [CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.643) total time= 1.7s [CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.647) total time= 1.4s [CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.649) total time= 1.3s [CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.621) total time= 1.2s [CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.629) total time= 1.3s [CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.651) total time= 1.6s [CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.942, test=0.631) total time= 1.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.670) total time= 0.6s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.657) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.672) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.629) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.652) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.659) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.661) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.673) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.631) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.653) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.641) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.655) total time= 0.8s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time= 0.5s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.676) total time= 0.6s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.632) total time= 0.6s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.653) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.655) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.649) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.638) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.655) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.646) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.654) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.668) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.834, test=0.627) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.646) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.653) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.644) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.648) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.640) total time= 0.6s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.659) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.649) total time= 0.6s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.661) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.669) total time= 0.9s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.630) total time= 0.8s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.645) total time= 0.8s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.661) total time= 0.8s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.647) total time= 0.9s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.645) total time= 1.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.637) total time= 0.9s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.658) total time= 1.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.653) total time= 1.5s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.659) total time= 1.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.668) total time= 1.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.630) total time= 1.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.646) total time= 1.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.660) total time= 1.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.651) total time= 1.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time= 0.0s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 2.8s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.667) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.646) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.655) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.660) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.669) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.654) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.698, test=0.643) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.662) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.660) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.676) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.634) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.652) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.666) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.657) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.645) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.663) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.654) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.678) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.637) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.651) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.668) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.653) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.655) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.646) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.662) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.654) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.661) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.679) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.639) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.652) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.667) total time= 0.5s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.656) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.648) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.637) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.653) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.639) total time= 0.5s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.650) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.667) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.628) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.639) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.646) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.640) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.639) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.633) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.651) total time= 0.8s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.640) total time= 0.7s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.650) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.668) total time= 0.7s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.629) total time= 0.7s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.636) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.650) total time= 0.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.641) total time= 0.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.631) total time= 1.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.626) total time= 1.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.647) total time= 1.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.643) total time= 1.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.647) total time= 1.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.659) total time= 1.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.625) total time= 1.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.630) total time= 1.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.653) total time= 1.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.638) total time= 1.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.651) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.670) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.657) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.653) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.640) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.659) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.672) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.629) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.651) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.652) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.652) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.640) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.659) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.661) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.673) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.688, test=0.632) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.664) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.652) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.655) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.641) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.695, test=0.654) total time= 0.6s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.661) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.676) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.696, test=0.632) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.653) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.665) total time= 0.5s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.654) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.648) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.637) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.658) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.646) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.655) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.668) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.626) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.645) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.653) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.645) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.648) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.639) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.660) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.649) total time= 0.6s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.661) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.670) total time= 0.6s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.631) total time= 0.9s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.644) total time= 1.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.661) total time= 2.5s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.648) total time= 1.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.647) total time= 2.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.636) total time= 2.0s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.659) total time= 1.7s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.653) total time= 1.5s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.659) total time= 1.5s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.669) total time= 1.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.631) total time= 1.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.866, test=0.647) total time= 1.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.660) total time= 1.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.652) total time= 1.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.642) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.653) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.656) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.644) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.660) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.633) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.652) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.654) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.644) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.663) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.655) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.661) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.676) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.697, test=0.633) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.665) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.651) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.656) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.646) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.664) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.654) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.662) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.678) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.636) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.652) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.669) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.653) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.656) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.648) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.664) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.655) total time= 0.9s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.661) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.679) total time= 0.8s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.636) total time= 0.6s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.652) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.668) total time= 0.5s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.655) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.647) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.635) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.654) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.638) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.652) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.671) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.628) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.639) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.649) total time= 0.5s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.645) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.642) total time= 0.8s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.631) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.652) total time= 0.7s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.641) total time= 0.6s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.653) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.669) total time= 0.7s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.628) total time= 0.7s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.638) total time= 0.7s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.654) total time= 0.8s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.640) total time= 0.9s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.635) total time= 1.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.626) total time= 1.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.649) total time= 1.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.640) total time= 1.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.949, test=0.649) total time= 1.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.660) total time= 1.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.627) total time= 1.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.631) total time= 1.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.650) total time= 1.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.639) total time= 1.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.628) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.629) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.652) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.648) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.658) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.645) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.631) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.669) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.657) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.651) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.640) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.660) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.672) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.629) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.652) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.653) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.640) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.660) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.662) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.673) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.687, test=0.632) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.664) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.641) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time= 0.5s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.661) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.675) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.694, test=0.633) total time= 0.6s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.665) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time= 0.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.651) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.638) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.657) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.648) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.656) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.671) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.630) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.646) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.656) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.648) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.651) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.816, test=0.640) total time= 0.8s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.659) total time= 1.0s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.650) total time= 1.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.661) total time= 1.0s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.671) total time= 1.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.632) total time= 1.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.647) total time= 1.0s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.663) total time= 1.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.652) total time= 1.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.649) total time= 1.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.637) total time= 1.8s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.662) total time= 1.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.654) total time= 1.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.659) total time= 1.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.670) total time= 1.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.632) total time= 1.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.648) total time= 1.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.662) total time= 1.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.655) total time= 1.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.642) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.649) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.647) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.634) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.657) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.649) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.650) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.637) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.654) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.661) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.654) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.643) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.655) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.660) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.675) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.634) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.651) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.651) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.656) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.646) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.655) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.677) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.635) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.666) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.653) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.657) total time= 0.5s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.645) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.664) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.655) total time= 0.5s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.663) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.677) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.636) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.652) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.666) total time= 0.5s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.658) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.650) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.638) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.656) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.645) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.655) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.668) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.630) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.643) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.654) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.643) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.648) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.636) total time= 0.6s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.653) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.645) total time= 0.6s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.884, test=0.657) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.667) total time= 0.6s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.634) total time= 0.6s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.642) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.654) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.644) total time= 0.7s [CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.929, test=0.641) total time= 1.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.632) total time= 1.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.650) total time= 1.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.649) total time= 1.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.928, test=0.650) total time= 1.0s [CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.660) total time= 1.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.634) total time= 1.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.636) total time= 1.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.653) total time= 1.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.644) total time= 1.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.638) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.642) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.650) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.646) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.632) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.654) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.648) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.642) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.648) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.637) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.655) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.642) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.649) total time= 0.5s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.640) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.655) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.652) total time= 0.6s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.656) total time= 0.5s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.670) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.630) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.648) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.643) total time= 0.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.632) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.635) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.648) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.641) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.652) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.660) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.620) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.643) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.649) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.642) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.635) total time= 0.8s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.849, test=0.633) total time= 0.9s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.847, test=0.649) total time= 0.8s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.644) total time= 0.7s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.653) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.662) total time= 0.7s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.848, test=0.622) total time= 0.8s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.640) total time= 0.7s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.852, test=0.652) total time= 0.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.643) total time= 0.7s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.635) total time= 1.5s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.878, test=0.640) total time= 1.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.650) total time= 1.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.867, test=0.642) total time= 1.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.652) total time= 1.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.872, test=0.664) total time= 1.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.877, test=0.622) total time= 1.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.874, test=0.636) total time= 1.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.878, test=0.651) total time= 1.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.871, test=0.640) total time= 1.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.664) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.645) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.630) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.649) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.667) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.629) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.643) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.635) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.632) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.662) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.649) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.639) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.658) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.653) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.657) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.668) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.648) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.658) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.644) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.651) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.644) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.660) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.713, test=0.653) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.659) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.673) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.632) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.714, test=0.651) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.661) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.647) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.650) total time= 0.5s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.646) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.662) total time= 0.6s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.656) total time= 0.5s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.657) total time= 0.5s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.675) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.750, test=0.641) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.753, test=0.648) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.663) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.748, test=0.649) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.882, test=0.634) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.888, test=0.628) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.643) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.878, test=0.640) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.647) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.651) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.614) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.888, test=0.630) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.652) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.635) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.626) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.935, test=0.630) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.642) total time= 0.7s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.639) total time= 0.6s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.922, test=0.642) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.653) total time= 0.7s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.937, test=0.613) total time= 0.7s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.937, test=0.624) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.649) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.636) total time= 0.7s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.616) total time= 1.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.974, test=0.629) total time= 1.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.975, test=0.641) total time= 1.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.974, test=0.634) total time= 1.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.636) total time= 1.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.642) total time= 12.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.979, test=0.609) total time= 2.7s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.612) total time= 2.8s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.649) total time= 2.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.633) total time= 2.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.637) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.626) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.8s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.7s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 1.0s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 1.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time= 1.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.8s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.642) total time= 0.8s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time= 0.9s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time= 1.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.645) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.632) total time= 0.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.655) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.651) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time= 0.5s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.664) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.627) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.674, test=0.647) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.653) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.643) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.648) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.637) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.654) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time= 0.8s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time= 0.5s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.667) total time= 0.9s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.628) total time= 1.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.647) total time= 0.7s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.656) total time= 0.9s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.643) total time= 0.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.649) total time= 1.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.640) total time= 1.4s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.656) total time= 1.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.652) total time= 1.0s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.656) total time= 1.0s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.670) total time= 1.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.629) total time= 1.0s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.648) total time= 1.0s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.658) total time= 1.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.644) total time= 1.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.635) total time= 0.8s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.634) total time= 1.0s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.649) total time= 1.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.826, test=0.639) total time= 1.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.653) total time= 0.8s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.660) total time= 0.8s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.620) total time= 0.9s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.644) total time= 0.7s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.837, test=0.649) total time= 0.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.643) total time= 0.7s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.637) total time= 1.5s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.847, test=0.632) total time= 1.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.845, test=0.650) total time= 1.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.643) total time= 1.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.654) total time= 1.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.662) total time= 1.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.620) total time= 1.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.846, test=0.641) total time= 1.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.850, test=0.654) total time= 1.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.842, test=0.642) total time= 1.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.869, test=0.637) total time= 2.0s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.638) total time= 2.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.873, test=0.649) total time= 2.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.641) total time= 2.0s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.868, test=0.652) total time= 1.9s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.664) total time= 2.0s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.875, test=0.623) total time= 1.9s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.872, test=0.639) total time= 48.9s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.876, test=0.653) total time= 0.9s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.870, test=0.640) total time= 0.8s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time= 0.0s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.630) total time= 0.0s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.0s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.650) total time= 0.0s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.0s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.665) total time= 0.0s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.6s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.645) total time= 0.0s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.646) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.630) total time= 0.0s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time= 0.0s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time= 0.0s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.0s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.669) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.629) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.643) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.636) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.653) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.632) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.653) total time= 0.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.650) total time= 0.0s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.653) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time= 0.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.668) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.632) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.648) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.644) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.652) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.643) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.660) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.652) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.671) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.716, test=0.633) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.650) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.664) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.649) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.653) total time= 0.5s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.647) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.661) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.751, test=0.653) total time= 0.8s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.658) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.671) total time= 1.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.751, test=0.637) total time= 1.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.749, test=0.647) total time= 0.8s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.665) total time= 1.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.651) total time= 1.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.632) total time= 1.0s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.633) total time= 1.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.887, test=0.644) total time= 1.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.877, test=0.639) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.879, test=0.649) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.877, test=0.653) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.886, test=0.618) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.885, test=0.634) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.883, test=0.649) total time= 0.5s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.880, test=0.640) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.926, test=0.627) total time= 0.8s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.928, test=0.635) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.927, test=0.643) total time= 0.8s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.922, test=0.637) total time= 0.7s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.647) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.652) total time= 0.7s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.930, test=0.620) total time= 0.8s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.929, test=0.633) total time= 0.7s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.925, test=0.649) total time= 0.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.643) total time= 0.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.619) total time= 1.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.626) total time= 1.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.639) total time= 1.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.973, test=0.635) total time= 1.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.967, test=0.642) total time= 1.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.643) total time= 1.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.977, test=0.620) total time= 1.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.975, test=0.625) total time= 1.8s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.644) total time= 1.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.638) total time= 1.6s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.627) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time= 0.1s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.629) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.662) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.642) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.645) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.650) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.653) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.664) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.647) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.643) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.647) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time= 0.5s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.667) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time= 0.4s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.656) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.643) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.649) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time= 0.8s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time= 0.8s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time= 0.7s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.655) total time= 0.6s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.670) total time= 0.6s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.648) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time= 0.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time= 0.7s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.640) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.636) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.649) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.641) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.653) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.659) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.621) total time= 0.6s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.643) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.648) total time= 0.4s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.645) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.641) total time= 0.8s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.638) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.650) total time= 1.0s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.645) total time= 0.9s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.656) total time= 1.0s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.663) total time= 0.8s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.624) total time= 0.8s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.642) total time= 0.9s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.654) total time= 0.8s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.644) total time= 0.9s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.641) total time= 1.5s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.644) total time= 1.6s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.649) total time= 1.9s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.644) total time= 1.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.652) total time= 1.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.666) total time= 1.7s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.623) total time= 1.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.640) total time= 2.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.654) total time= 2.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.642) total time= 3.8s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.640) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.631) total time= 0.6s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.649) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.664) total time= 0.1s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.1s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.646) total time= 0.2s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.629) total time= 0.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.2s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.670) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.658) total time= 0.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.642) total time= 0.2s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time= 0.4s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time= 0.3s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time= 0.1s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.637) total time= 0.1s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time= 0.1s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.652) total time= 0.2s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.658) total time= 0.2s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.667) total time= 0.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.631) total time= 0.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.649) total time= 0.2s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time= 0.1s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.646) total time= 0.1s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.653) total time= 0.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.644) total time= 0.3s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.660) total time= 0.3s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.651) total time= 0.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.660) total time= 0.3s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.672) total time= 0.3s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.711, test=0.632) total time= 0.3s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time= 0.3s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.664) total time= 0.3s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.649) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.654) total time= 0.6s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.648) total time= 0.8s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.662) total time= 0.8s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.655) total time= 0.5s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.662) total time= 0.5s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.674) total time= 0.5s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.640) total time= 0.6s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.651) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.666) total time= 0.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.654) total time= 0.5s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.641) total time= 0.4s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.640) total time= 0.5s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.647) total time= 0.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.635) total time= 0.4s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.861, test=0.651) total time= 0.4s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.656) total time= 0.4s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.621) total time= 0.5s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.639) total time= 0.5s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.648) total time= 0.6s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.637) total time= 0.4s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.636) total time= 0.7s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.644) total time= 0.7s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.902, test=0.649) total time= 0.7s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.639) total time= 0.8s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.647) total time= 0.7s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.658) total time= 0.8s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.620) total time= 0.8s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.636) total time= 0.6s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.649) total time= 0.7s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.639) total time= 0.8s [CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.632) total time= 1.3s [CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.643) total time= 1.2s [CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.646) total time= 1.4s [CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.640) total time= 1.3s [CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.644) total time= 1.1s [CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.653) total time= 1.2s [CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.619) total time= 1.2s [CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.632) total time= 1.1s [CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.949, test=0.649) total time= 1.2s [CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.639) total time= 1.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.648) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.651) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.648) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.631) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.650, test=0.669) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.625) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.652) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.640) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.661) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.652) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.660) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.671) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.629) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.661) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.652) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.640) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.660) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.662) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.673) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.632) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.664) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.654) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.640) total time= 0.8s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time= 0.7s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time= 0.7s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.661) total time= 0.7s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.676) total time= 0.7s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.632) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.665) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.653) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.640) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.661) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.647) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.657) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.669) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.628) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.646) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.655) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.650) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.653) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.640) total time= 0.7s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.662) total time= 0.7s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.806, test=0.650) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.661) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.668) total time= 0.7s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.632) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.648) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.662) total time= 0.7s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.651) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.835, test=0.649) total time= 1.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.639) total time= 1.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.662) total time= 1.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.653) total time= 1.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.836, test=0.659) total time= 1.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.835, test=0.670) total time= 1.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.835, test=0.632) total time= 1.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.836, test=0.649) total time= 1.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.837, test=0.662) total time= 1.5s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.654) total time= 1.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.650) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.654) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.662) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.654) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.696, test=0.644) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.655) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.661) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.676) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.635) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.652) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.664) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.650) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.645) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.663) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.678) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.635) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.651) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.666) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.651) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.655) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.647) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.664) total time= 0.6s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.657) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.663) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.676) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.637) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.649) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.665) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.655) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.648) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.840, test=0.640) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.844, test=0.656) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.840, test=0.643) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.657) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.840, test=0.671) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.842, test=0.630) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.634) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.653) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.843, test=0.643) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.648) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.878, test=0.639) total time= 0.6s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.883, test=0.658) total time= 0.6s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.642) total time= 0.7s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.657) total time= 0.7s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.669) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.634) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.632) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.882, test=0.655) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.643) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.639) total time= 1.4s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.633) total time= 1.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.654) total time= 1.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.643) total time= 1.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.652) total time= 1.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.661) total time= 1.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.633) total time= 1.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.627) total time= 1.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.651) total time= 1.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.641) total time= 1.0s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.629) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.661) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.625) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.648) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.651) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.648) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.631) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.649) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.654) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.650, test=0.669) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.625) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.652) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.640) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.661) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.652) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.660) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.671) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.628) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.662) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.651) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.653) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.640) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.662) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.673) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.632) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.664) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.652) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.655) total time= 0.7s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.640) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.661) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.675) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.632) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.653) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.665) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.655) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.653) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.640) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.661) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.648) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.657) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.670) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.628) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.646) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.653) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.650) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.653) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.641) total time= 0.7s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.661) total time= 0.7s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.650) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.806, test=0.661) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.669) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.631) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.648) total time= 0.7s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.659) total time= 0.7s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.652) total time= 0.7s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.650) total time= 1.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.639) total time= 1.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.663) total time= 1.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.654) total time= 1.4s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.659) total time= 1.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.671) total time= 1.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.633) total time= 1.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.649) total time= 1.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.834, test=0.661) total time= 1.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.832, test=0.655) total time= 1.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.632) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.649) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.627) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.650) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.636) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.654) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.661) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.660) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.654) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.695, test=0.643) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.663) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.655) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.661) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.676) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.634) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.652) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.664) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.693, test=0.650) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.645) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.663) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.678) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.635) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.651) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.666) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.652) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.654) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.648) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.663) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.658) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.662) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.675) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.637) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.650) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.665) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.654) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.653) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.837, test=0.641) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.656) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.641) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.653) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.671) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.628) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.636) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.843, test=0.652) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.643) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.646) total time= 0.7s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.638) total time= 0.6s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.881, test=0.656) total time= 0.7s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.874, test=0.641) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.655) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.668) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.632) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.635) total time= 0.7s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.878, test=0.656) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.641) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.641) total time= 1.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.631) total time= 1.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.649) total time= 1.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.929, test=0.642) total time= 1.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.928, test=0.651) total time= 1.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.659) total time= 1.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.633) total time= 1.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.629) total time= 1.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.656) total time= 1.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.639) total time= 1.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.628) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.652) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.647) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.651) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.662) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.626) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.647) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.655) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.640) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.642) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.630) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.652) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.647) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.648, test=0.663) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.626) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.650, test=0.648) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.639) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.630) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.653) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.649) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.650, test=0.669) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.657) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.641) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.652) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.640) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.660) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.652) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.662) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.671) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.629) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.651) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.662) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.650) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.653) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.640) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.660) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.652) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.663) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.672) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.632) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.652) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.664) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.651) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.655) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.640) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.662) total time= 0.6s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.654) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.663) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.675) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.633) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.653) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.666) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.655) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.654) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.639) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.661) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.649) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.657) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.669) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.630) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.647) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.656) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.650) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.790, test=0.653) total time= 0.7s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.788, test=0.640) total time= 0.7s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.789, test=0.662) total time= 0.6s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.788, test=0.649) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.789, test=0.661) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.792, test=0.670) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.789, test=0.632) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.791, test=0.649) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.792, test=0.663) total time= 0.7s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.792, test=0.653) total time= 0.7s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.652) total time= 1.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.640) total time= 1.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.664) total time= 1.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.814, test=0.654) total time= 1.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.660) total time= 1.4s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.814, test=0.671) total time= 1.4s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.632) total time= 1.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.651) total time= 1.3s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.662) total time= 1.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.814, test=0.655) total time= 1.5s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.631) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.650) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.664) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.628) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.653, test=0.648) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.655) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.633) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.657) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.651) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.668) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.630) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.636) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.654) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.670) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.654) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.694, test=0.643) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.664) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.653) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.661) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.674) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.634) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.652) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.665) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.649) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.657) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.664) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.654) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.664) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.676) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.637) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.652) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.667) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.652) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.657) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.647) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.667) total time= 0.6s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.656) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.665) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.676) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.638) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.651) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.667) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.655) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.651) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.638) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.655) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.644) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.657) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.672) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.630) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.638) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.824, test=0.658) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.824, test=0.642) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.649) total time= 1.3s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.638) total time= 0.9s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.656) total time= 1.2s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.645) total time= 1.0s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.659) total time= 0.8s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.670) total time= 0.8s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.631) total time= 0.8s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.858, test=0.640) total time= 0.8s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.661) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.643) total time= 0.9s [CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.645) total time= 2.1s [CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.633) total time= 2.4s [CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.907, test=0.653) total time= 1.9s [CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.644) total time= 2.0s [CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.655) total time= 1.3s [CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.664) total time= 1.5s [CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.630) total time= 1.1s [CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.911, test=0.634) total time= 1.4s [CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.655) total time= 1.4s [CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.643) total time= 1.1s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.626) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.628) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.645) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.8s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.643) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.637) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.645) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.650) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.652) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.665) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.649) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.643) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.655) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.668) total time= 0.7s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time= 1.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.656) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.643) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time= 1.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time= 1.0s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time= 1.0s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.651) total time= 0.8s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time= 0.8s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.670) total time= 0.7s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time= 0.7s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.647) total time= 0.8s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time= 0.7s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.638) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.637) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.646) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.643) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.651) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.660) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.621) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.643) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.648) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.646) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.640) total time= 1.1s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.636) total time= 0.9s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.648) total time= 0.9s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.646) total time= 0.9s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.655) total time= 0.9s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.664) total time= 1.0s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.625) total time= 1.0s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.641) total time= 0.9s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.652) total time= 0.9s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.644) total time= 0.9s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.640) total time= 1.8s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.642) total time= 1.6s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.648) total time= 1.9s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.645) total time= 1.5s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.653) total time= 1.7s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.665) total time= 1.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.623) total time= 1.8s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.640) total time= 1.9s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.864, test=0.652) total time= 2.1s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.643) total time= 1.6s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.641) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.630) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.649) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.665) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.628) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.655) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.650) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.669) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.642) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.653) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.671) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.650) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.639) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.658) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.652) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.658) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.668) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.629) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.648) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.646) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.653) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.642) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.651) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.659) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.673) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.631) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.651) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.662) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.649) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.653) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.649) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.743, test=0.663) total time= 0.7s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.656) total time= 0.7s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.662) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.674) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.747, test=0.639) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.650) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.664) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.649) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.634) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.636) total time= 0.7s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.643) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.635) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.655) total time= 0.7s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.657) total time= 0.7s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.617) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.641) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.652) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.644) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.627) total time= 0.7s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.924, test=0.637) total time= 1.0s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.641) total time= 0.8s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.640) total time= 0.8s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.654) total time= 0.8s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.654) total time= 0.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.618) total time= 0.9s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.635) total time= 0.8s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.919, test=0.654) total time= 1.1s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.641) total time= 0.8s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.615) total time= 1.3s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.631) total time= 1.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.971, test=0.638) total time= 1.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.635) total time= 1.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.970, test=0.650) total time= 1.6s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.645) total time= 1.5s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.617) total time= 1.7s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.628) total time= 1.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.972, test=0.648) total time= 1.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.638) total time= 1.5s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.626) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.646) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.628) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.645) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.647) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.637) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.639) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.626) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.643) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.637) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.645) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.652) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.665) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.627) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.673, test=0.648) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.654) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.643) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.636) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.654) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.652) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.655) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.668) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.647) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.657) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.644) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.648) total time= 0.7s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.639) total time= 0.7s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.655) total time= 0.6s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.651) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time= 0.7s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.670) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.648) total time= 0.7s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.658) total time= 0.7s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.644) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.639) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.638) total time= 0.6s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.647) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.643) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.650) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.659) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.621) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.644) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.823, test=0.648) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.646) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.640) total time= 0.9s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.637) total time= 0.9s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.649) total time= 0.9s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.646) total time= 0.8s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.654) total time= 1.0s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.664) total time= 1.0s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.626) total time= 1.0s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.642) total time= 0.8s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.652) total time= 0.9s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.645) total time= 0.9s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.640) total time= 1.8s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.642) total time= 1.6s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.649) total time= 1.7s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.644) total time= 1.8s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.653) total time= 2.7s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.664) total time= 2.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.625) total time= 2.6s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.642) total time= 1.9s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.652) total time= 1.6s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.643) total time= 2.0s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.641) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.630) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.665) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.627) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.629) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.651) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.668) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.629) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time= 0.3s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.637) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.657) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.651) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.656) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.668) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.630) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.648) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.645) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.652) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.643) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.660) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.652) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.657) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.673) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.712, test=0.631) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.649) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.662) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.651) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.744, test=0.652) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.646) total time= 0.6s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.743, test=0.663) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.743, test=0.654) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.658) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.675) total time= 0.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.746, test=0.637) total time= 0.6s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.646) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.745, test=0.661) total time= 0.6s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.653) total time= 0.6s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.638) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.635) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.875, test=0.649) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.638) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.651) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.655) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.622) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.637) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.652) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.639) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.634) total time= 0.8s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.637) total time= 0.8s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.643) total time= 0.7s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.640) total time= 0.8s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.652) total time= 0.7s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.653) total time= 0.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.625) total time= 0.8s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.919, test=0.633) total time= 0.9s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.653) total time= 0.7s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.912, test=0.637) total time= 0.7s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.963, test=0.621) total time= 1.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.628) total time= 1.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.638) total time= 1.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.967, test=0.640) total time= 1.5s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.649) total time= 1.5s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.646) total time= 1.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.626) total time= 1.6s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.969, test=0.624) total time= 1.5s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.648) total time= 1.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.634) total time= 1.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.638) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.626) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.648) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.640, test=0.656) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.625) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.647) total time= 0.1s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.647) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time= 0.1s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.639) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.646, test=0.628) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.649) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.647) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.659) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.625) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.646) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.646) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.637) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.638) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.630) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.652) total time= 0.5s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.644, test=0.661) total time= 0.4s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.627) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.642) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.651) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.638) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.646) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.631) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.654) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.653) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.665) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.629) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.649) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.654) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.644) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.648) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.635) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.654) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.652) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.655) total time= 0.3s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.668) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.628) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.647) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.645) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time= 0.7s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.638) total time= 0.9s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.655) total time= 1.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.652) total time= 1.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.656) total time= 0.9s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.671) total time= 0.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.630) total time= 0.8s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.648) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.658) total time= 1.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.645) total time= 2.0s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.642) total time= 1.0s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.638) total time= 1.0s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.650) total time= 2.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.646) total time= 0.9s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.650) total time= 0.8s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.660) total time= 0.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.622) total time= 1.6s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.646) total time= 0.6s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.646) total time= 0.7s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.648) total time= 0.7s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.643) total time= 1.0s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.637) total time= 0.9s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.650) total time= 0.9s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.646) total time= 1.0s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.654) total time= 1.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.664) total time= 1.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.812, test=0.625) total time= 0.8s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.645) total time= 0.9s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.653) total time= 0.9s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.814, test=0.645) total time= 0.8s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.837, test=0.644) total time= 1.6s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.642) total time= 1.7s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.650) total time= 1.7s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.836, test=0.646) total time= 1.6s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.837, test=0.655) total time= 1.6s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.840, test=0.665) total time= 1.8s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.625) total time= 1.6s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.644) total time= 1.8s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.653) total time= 1.6s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.644) total time= 1.7s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.630) total time= 0.1s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.650) total time= 0.1s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time= 0.1s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.651) total time= 0.1s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.665) total time= 0.1s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.627) total time= 0.1s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time= 0.1s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.638) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time= 0.2s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.631) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.649) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.669) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.642) total time= 0.2s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time= 0.4s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.637) total time= 0.4s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time= 0.5s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.652) total time= 0.5s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.661) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.671) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.630) total time= 0.4s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time= 0.4s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.648) total time= 0.1s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.637) total time= 0.2s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.657) total time= 0.2s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.652) total time= 0.2s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.655) total time= 0.2s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.667) total time= 0.2s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.629) total time= 0.2s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.646) total time= 0.2s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.658) total time= 0.2s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.646) total time= 0.3s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.651) total time= 0.3s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.643) total time= 0.3s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.661) total time= 0.3s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.650) total time= 0.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.670) total time= 0.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.631) total time= 0.3s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.649) total time= 0.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.662) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.651) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.651) total time= 0.6s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.644) total time= 0.6s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.663) total time= 0.6s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.654) total time= 0.6s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.660) total time= 0.6s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.673) total time= 0.6s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.638) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.645) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.663) total time= 0.5s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.653) total time= 0.5s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.642) total time= 0.5s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.639) total time= 0.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.859, test=0.649) total time= 0.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.641) total time= 0.4s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.847, test=0.651) total time= 0.4s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.655) total time= 0.5s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.623) total time= 0.5s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.637) total time= 0.5s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.648) total time= 0.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.646) total time= 0.4s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.634) total time= 0.7s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.642) total time= 0.8s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.646) total time= 0.8s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.645) total time= 0.7s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.651) total time= 0.7s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.896, test=0.656) total time= 0.7s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.621) total time= 0.8s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.637) total time= 1.1s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.652) total time= 0.9s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.645) total time= 0.9s [CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.625) total time= 2.0s [CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.948, test=0.639) total time= 1.5s [CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.640) total time= 1.4s [CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.643) total time= 1.3s [CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.646) total time= 1.3s [CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.651) total time= 1.3s [CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.624) total time= 1.6s [CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.625) total time= 1.3s [CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.647) total time= 1.4s [CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.644) total time= 1.4s
C:\Users\woowe\anaconda\Lib\site-packages\numpy\ma\core.py:2820: RuntimeWarning: invalid value encountered in cast _data = np.array(data, dtype=dtype, copy=copy,
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None, device=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
grow_policy=None, importance_ty...
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None,
n_jobs=None, num_parallel_tree=None,
random_state=42, ...),
param_grid={'alpha': [0, 0.1, 1], 'colsample_bytree': [0.3, 0.7],
'lambda': [0, 0.1, 1], 'learning_rate': [0.01, 0.05],
'max_depth': [2, 5, 10],
'n_estimators': [50, 100, 200]},
return_train_score=True, scoring='roc_auc', verbose=4)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),
estimator=XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None, device=None,
early_stopping_rounds=None,
enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None,
grow_policy=None, importance_ty...
max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=None,
n_jobs=None, num_parallel_tree=None,
random_state=42, ...),
param_grid={'alpha': [0, 0.1, 1], 'colsample_bytree': [0.3, 0.7],
'lambda': [0, 0.1, 1], 'learning_rate': [0.01, 0.05],
'max_depth': [2, 5, 10],
'n_estimators': [50, 100, 200]},
return_train_score=True, scoring='roc_auc', verbose=4)XGBClassifier(alpha=1, base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.3, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, lambda=1, learning_rate=0.05,
max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=5, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=200, n_jobs=None, ...)XGBClassifier(alpha=1, base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.3, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, grow_policy=None, importance_type=None,
interaction_constraints=None, lambda=1, learning_rate=0.05,
max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=5, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=200, n_jobs=None, ...)report_GridSearchCV_results(grid_search_xgb)
- Best combination of hyperparameters:
{'alpha': 1, 'colsample_bytree': 0.3, 'lambda': 1, 'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 200}
- Best mean_test_score:
0.6578152413134496
- Score by fold for best estimator:
[0.6567652743652744, 0.6472609336609336, 0.6667059787059788, 0.6559855855855855, 0.6648111384111384, 0.6761212121212121, 0.638134414012138, 0.6507394945651604, 0.6667289026550527, 0.6548994790520215]
- Top 10 hyperparameter combinations by mean_test_score:
| mean_test_score | param_colsample_bytree | param_n_estimators | param_max_depth | param_alpha | param_lambda | param_learning_rate | |
|---|---|---|---|---|---|---|---|
| rank_test_score | |||||||
| 1 | 0.657815 | 0.3 | 200 | 5 | 1.0 | 1.0 | 0.05 |
| 2 | 0.657519 | 0.3 | 200 | 5 | 0.0 | 1.0 | 0.05 |
| 3 | 0.657363 | 0.3 | 200 | 5 | 0.0 | 0.0 | 0.05 |
| 4 | 0.657353 | 0.3 | 200 | 5 | 0.1 | 0.1 | 0.05 |
| 5 | 0.657237 | 0.3 | 200 | 5 | 0.0 | 0.1 | 0.05 |
| 6 | 0.657202 | 0.3 | 200 | 5 | 0.1 | 1.0 | 0.05 |
| 7 | 0.657145 | 0.3 | 200 | 5 | 0.1 | 0.0 | 0.05 |
| 8 | 0.657091 | 0.3 | 100 | 5 | 0.1 | 0.1 | 0.05 |
| 9 | 0.656973 | 0.3 | 100 | 5 | 0.0 | 0.1 | 0.05 |
| 10 | 0.656893 | 0.3 | 100 | 5 | 0.1 | 0.0 | 0.05 |
compare_performance(grid_search_xgb)
| train_AUC | val_AUC | |
|---|---|---|
| 1 | 0.649362 | 0.644820 |
| 2 | 0.650011 | 0.645682 |
| 3 | 0.652294 | 0.646945 |
| 4 | 0.684376 | 0.652973 |
| 5 | 0.686990 | 0.653782 |
| 6 | 0.694554 | 0.655196 |
| 7 | 0.831597 | 0.648039 |
| 8 | 0.840301 | 0.650964 |
| 9 | 0.868222 | 0.650752 |
| 10 | 0.652286 | 0.647157 |
| Mean | 0.720999 | 0.649631 |
best_model_xgb=grid_search_xgb.best_estimator_
plot_feature_importance_chart(best_model_xgb, X_train, y_train, cv, "XGBoost")
evaluate_model(best_model_xgb, X_test, y_test)
Test AUC: 0.66 Accuracy: 0.62 Confusion Matrix: [[2944 1056] [1821 1679]]
Classification Report:
precision recall f1-score support
0 0.62 0.74 0.67 4000
1 0.61 0.48 0.54 3500
accuracy 0.62 7500
macro avg 0.62 0.61 0.61 7500
weighted avg 0.62 0.62 0.61 7500
plot_roc_curve(best_model_xgb, X_test, y_test)